We run 13 Optuna trials with TPE sampling for CITA_INSTRUCT. TPE proposes configurations by fitting
densities over **good** vs. **bad** trials and sampling where expected improvement is highest. We optimize
learning rate, weight decay, _β_, and _λ_ KL. Figure 16 summarizes the sensitivity of reward margin to these
hyperparameters, revealing a concentrated “good” region rather than a broad plateau.
**Reading the 4-panel ablation.** Figure 16 presents reward margin (y-axis) against each of the four tuned
hyperparameters (x-axis): _β_ (DPO temperature), _λ_ KL (KL regularization coefficient), learning rate, and
weight decay. Each blue point represents one of 13 Optuna trials, with Trial 7 ( _⋆_ ) highlighted as the
best configuration. Dashed trend lines (polynomial fits) reveal the functional relationship between each
hyperparameter and reward margin. The key observation is that reward margin is **not uniformly sensitive**
to all hyperparameters: learning rate shows a strong positive correlation (higher LR _→_ higher margin up
to a point), while weight decay shows weaker sensitivity with high variance.
**Identifying the “Goldilocks zone”.** Across all four panels, Trial 7 consistently appears near the peak of
the trend curve, confirming that the selected configuration occupies the **intersection of optimal regions**
across hyperparameters. The _β_ panel shows a peaked distribution with optimum around 0.10–0.12; the
_λ_ KL panel shows an inverted-U with optimum around 2–3 _×_ 10 _[−]_ [4] ; the learning rate panel shows monotonic
increase up to _∼_ 5 _×_ 10 _[−]_ [6] ; and the weight decay panel shows weak sensitivity. This multi-dimensional
view explains why naive grid search would be inefficient: the optimal region is a narrow **tube** in 4D space,
not a hyperrectangle.
**Why TPE sampling works.** The Tree-structured Parzen Estimator (TPE) efficiently navigates this
complex landscape by modeling _P_ (hyperparameters _|_ good) vs. _P_ (hyperparameters _|_ bad). Trials 0–4
explore broadly; trials 5–12 concentrate near promising regions. The final distribution of points shows
TPE’s adaptive behavior: dense sampling near _β ≈_ 0 _._ 11, _λ_ KL _≈_ 2 _×_ 10 _[−]_ [4], and LR _≈_ 5 _×_ 10 _[−]_ [6], with
sparse exploration of poor regions (low LR, extreme _β_ ).
**Pareto structure.** Because reward margin and preference accuracy do not perfectly correlate with
ECLIPTICA switchability, we additionally analyze margin–accuracy trade-offs. Figure 17 shows the
Pareto frontier; the selected configuration lies on the frontier, indicating that further margin gains typically
require accuracy/switchability trade-offs.
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Figure 16: **Hyperparameter sensitivity across 13 Optuna trials.** Reward margin is most sensitive to learning
rate and the ( _β, λ_ KL) interaction. Trial 7 ( _⋆_ ) achieves the best trade-off, aligning with the “Goldilocks” band:
_β ∈_ [0 _._ 10 _,_ 0 _._ 12], _λ_ KL _≈_ 2 _×_ 10 _[−]_ [4], and LR _≈_ 5 _×_ 10 _[−]_ [6] .
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Figure 17: **Pareto frontier across trials.** Trial 7 lies on the frontier, achieving near-optimal balance (margin=7.52,
accuracy=89%) while maintaining strong instruction-conditioned switching.
**Interpreting the Pareto frontier.** Figure 17 plots the two primary training objectives—reward margin
(x-axis, higher is better) and preference accuracy (y-axis, higher is better)—for all 13 Optuna trials.
The red dashed line traces the **Pareto frontier** : configurations where no other trial dominates on both
objectives. Trials below or left of this frontier are Pareto-dominated (another trial is better on at least one
axis without being worse on the other). The frontier reveals a fundamental trade-off: achieving margin
_>_ 9 (trials 0, 12) requires sacrificing accuracy to _∼_ 85–87%, while maintaining accuracy _>_ 89% (trials 2,
8) limits margin to _∼_ 7.
**Why Trial 7 is optimal for switching.** Trial 7 ( _⋆_ ) achieves margin 7.52 with accuracy 89%, placing
it **on the Pareto frontier** at the “knee” where both objectives are near-optimal. Crucially, downstream
evaluation shows that Trial 7 produces the best ECLIPTICA switching scores, suggesting that the marginaccuracy balance at this point corresponds to **optimal instruction-conditioned behavior** . Trials 0 and
12 achieve higher margins (9.9, 10.1) but their lower accuracy (87%, 86%) correlates with degraded
switching—the model over-specializes to one preference direction, losing the ability to traverse between
instruction-conditioned regimes.
**Multi-objective optimization justification.** This Pareto analysis justifies our multi-objective Optuna
formulation (maximize margin, maximize accuracy, minimize eval loss). Single-objective optimization
on margin alone would select trials 0 or 12, which underperform on switching despite high margins. By
optimizing for the Pareto frontier and selecting configurations at the knee, we ensure that CITA learns
**balanced, traversable** preference geometry rather than collapsing to a single dominant regime.
**Trial 7 analysis (why it wins).** The best configuration ( _β_ =0.107, _λ_ KL=0.00023, LR=5.4e-6) occupies a
narrow region where: **(i)** _β_ **is high enough** to separate instruction-conditioned preferences, **(ii)** _λ_ **KL is**
**strong enough** to prevent regime drift, and **(iii) LR is low enough** to avoid overshooting under longer
instruction-augmented sequences. This “alignment” of the three levers yields high margins without
sacrificing switchability.

H.3 Hyperparameter Sensitivity (Optuna/TPE)


Figures 18–20 provide per-parameter sensitivity analyses, plotting reward margin, accuracy, and evaluation
loss. Across plots, Trial 7 ( _⋆_ ) consistently appears near the optimal region, indicating that the final choice
is not an outlier but a coherent solution across axes.
**Understanding** _β_ **(DPO temperature).** Figure 18 shows how the DPO temperature parameter _β_ affects
three training metrics across 13 Optuna trials. _β_ controls the **sharpness of preference learning** : higher _β_
increases the penalty for ranking violations, pushing the model to more decisively separate chosen from
rejected responses. The top-left panel (Reward Margin) reveals a clear **inverted-U relationship** : margin
is low at extreme _β_ values and peaks in the 0.10–0.12 range. At _β <_ 0 _._ 10, preference learning is too soft
and the model fails to discriminate strongly; at _β >_ 0 _._ 14, over-sharpening causes the model to collapse
toward a single dominant response pattern, destroying multi-regime switching.
_β_ **’s differential effects on metrics.** The top-right panel (Accuracy) shows remarkable **stability** across
the _β_ range: accuracy varies only from 82% to 89%, with no clear trend. This indicates that _β_ primarily
affects **separation magnitude** rather than **ranking correctness** —the model can correctly rank preferences
at any _β_, but the confidence of that ranking varies. The bottom panel (Eval Loss) shows an upward trend:
higher _β_ increases loss, consistent with the over-sharpening hypothesis. Trials 0 and 12 achieve very
high margins (9.9, 10.1) but also high loss (0.51, 0.61), indicating unstable training. Trial 7 at _β_ =0.107
achieves strong margin (7.5) with low loss (0.33), representing the optimal balance.
**Understanding** _λ_ **KL (KL regularization).** Figure 19 shows how the KL regularization coefficient
_λ_ KL affects training metrics. _λ_ KL controls the **stability anchor** in CITA’s unified loss: it penalizes
deviation from the reference policy (frozen DPO checkpoint), preventing the model from drifting too
far during instruction-conditioned training. The top-left panel (Reward Margin) shows a pronounced
**inverted-U relationship** : margin peaks around _λ_ KL _≈_ 2–4 _×_ 10 _[−]_ [4] and drops sharply at both extremes.
At _λ_ KL _<_ 1 _._ 5 _×_ 10 _[−]_ [4], the anchor is too weak to prevent regime interference, causing unstable training
and low margins; at _λ_ KL _>_ 5 _×_ 10 _[−]_ [4], the anchor is too strong and suppresses preference learning entirely.
_λ_ **KL’s effect on accuracy and loss.** The top-right panel (Accuracy) shows a **downward trend** : higher
_λ_ KL reduces accuracy from _∼_ 89% to _∼_ 82%. This is intuitive: stronger KL regularization constrains
the policy closer to the reference, limiting its ability to learn new preference patterns and thus reducing
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Figure 18: _β_ **sensitivity.** Reward margin peaks at _β ≈_ 0 _._ 10–0 _._ 12; accuracy is stable (88–90%), while evaluation
loss increases at higher _β_, consistent with over-sharpening. Trial 7 ( _⋆_ ) at _β_ =0.107 achieves strong margins while
preserving switchability.
ranking accuracy. The bottom panel (Eval Loss) shows an **upward trend** : higher _λ_ KL increases loss,
reflecting the added KL penalty term in the unified loss. Trial 7 at _λ_ KL=0.00023 achieves the optimal
trade-off: strong enough regularization to maintain stability (low loss 0.33) while preserving learning
capacity (high accuracy 89%, margin 7.5).
**Why the KL anchor is mandatory for CITA.** The sharp margin drop at low _λ_ KL (trials 1, 3, 4)
demonstrates that the KL anchor is **not optional** . Without sufficient regularization, CITA training
becomes unstable because the model can drift arbitrarily from the DPO-pretrained policy, losing the
preference structure that enables multi-regime switching. This finding validates a core design choice:
CITA requires explicit anchoring to prevent the instruction-conditioning optimization from destroying the
base preference geometry.
**Learning rate is the dominant sensitivity axis.** Figure 20 reveals that learning rate has the **strongest**
**effect** on reward margin of any hyperparameter. The top-left panel shows a clear **monotonic positive**
**relationship** : margin increases from _∼_ 3.5 at LR=2 _._ 5 _×_ 10 _[−]_ [6] to _∼_ 10 at LR=5 _._ 4 _×_ 10 _[−]_ [6] . This strong
correlation (dashed trend line has high _R_ [2] ) indicates that LR directly controls the “aggressiveness”
of preference learning. However, the highest-margin trials (0, 12 at LR _≈_ 5.2–5.4 _×_ 10 _[−]_ [6] ) also exhibit
degraded switching on ECLIPTICA, suggesting that very high LR causes over-fitting to preference pairs
at the expense of multi-regime flexibility.
**LR’s effect on accuracy and loss.** The top-right panel (Accuracy) shows a **positive correlation** : higher
LR improves accuracy from _∼_ 82% to _∼_ 89%. Unlike _β_ and _λ_ KL (where accuracy was relatively stable),
LR directly affects how well the model learns to rank preferences correctly. The bottom panel (Eval Loss)
shows a **U-shaped relationship** : loss is minimized around LR=4 _._ 5–5 _._ 2 _×_ 10 _[−]_ [6], increasing at both lower
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Figure 19: _λ_ **KL sensitivity.** Reward margin exhibits an inverted-U: too low produces instability and regime
interference; too high suppresses learning. Accuracy is comparatively stable, indicating the dominant effect is on
switching stability rather than raw preference fit. Trial 7 ( _⋆_ ) at _λ_ KL=0.00023 lies in the optimal band.
LR (underfitting) and higher LR (overfitting/instability). Trial 7 at LR=5 _._ 4 _×_ 10 _[−]_ [6] sits at the edge of the
optimal region—slightly higher would risk instability.
**Why CITA_INSTRUCT requires lower LR than standard DPO.** A key insight from this ablation
is that instruction-augmented training requires _∼_ 50% lower LR than NoInstruct variants. The reason is
sequence length: Instruct samples include system prompts, adding 30–40% more tokens per sequence.
Longer sequences produce larger gradient magnitudes (more terms in the loss sum), which can cause
overshooting if LR is not reduced proportionally. Our Optuna search space accounts for this by using a
conditional upper bound: if variant == "Instruct": lr_max = 0.5 _×_ lr_max_NoInstruct. This
constraint ensures that Trial 7’s LR of 5 _._ 4 _×_ 10 _[−]_ [6] is appropriate for the longer Instruct sequences.
**Overall takeaway.** Switchability emerges only when **separation** (controlled by _β_ ) and **stability** (controlled by _λ_ KL and LR) are jointly satisfied. The ablations show that maximizing reward margin alone is
insufficient; the best instruction-driven alignment requires the **Goldilocks zone** where regimes remain
**distinct yet co-located**, enabling reliable counterfactual switching under a fixed user prompt.
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Figure 20: **Learning-rate sensitivity.** Reward margin is highly sensitive to LR, with an optimum near 5 _×_ 10 _[−]_ [6] .
Higher LR reduces accuracy and increases loss, especially for instruction-augmented sequences. **Key insight:**
CITA_INSTRUCT requires _∼_ 50% lower LR than standard DPO due to longer contexts and higher gradient variance.

ECLIPTICA -- A Framework for Switchable LLM Alignment via CITA - Contrastive Instruction-Tuned Alignment

H.3.1 Individual Hyperparameter Analysis

Kapil Wanaskar
Gaytri Jena
Vinija Jain
Aman Chadha
Amitava Das


This appendix expands the evaluation beyond aggregate scores, clarifying **what kind of capability** CITA
adds and **how to interpret** improvements in the ECLIPTICA setting. Our thesis is that instruction-driven
alignment should be assessed as **counterfactual policy control** : holding the **user request** fixed while
changing only the **alignment instruction contract** . Accordingly, we organize the evaluation into four
operational properties that matter in deployment: **generalization**, **robustness**, **over-refusal calibration**,
and **policy switching** . For each, we state (i) what we measure, (ii) what success looks like, and (iii) what
failure modes look like.


**Claim.** CITA learns an instruction-conditioned policy family that **transfers** to prompts and topics not
observed during training.
**What we measure.** We evaluate whether an alignment instruction _I_ induces the intended behavioral
posture on **novel prompts** : (i) unseen domains (e.g., finance _→_ health advice posture, crypto _→_ workplace
communication), (ii) different surface forms (question, imperative, multi-part), (iii) distributional shifts in
topic and framing.
**What success looks like.** A generalized policy does **not** memorize prompt templates. Instead, it applies
**contract-consistent content selection** : a safety_first instruction should reliably increase warnings
and guardrails, a concise instruction should reliably compress while preserving key decision variables,
and a professional instruction should produce consistent structure and hedging behavior. Importantly,
this should hold even when the prompt is phrased adversarially or indirectly.
**What failure looks like.** We observe three typical generalization failures in instruction-conditioned
systems: **(i) template lock-in** (works on familiar formats, fails on paraphrases), **(ii) topic leakage**
(instruction affects only some domains), **(iii) shallow style compliance** (tone changes but policy does
not). ECLIPTICA’s fixed-prompt counterfactual design is explicitly meant to detect (iii).

Generalization: instruction-conditioned policy transfer


**Claim.** A switchable alignment mechanism must remain stable under **jailbreak-like perturbations** and
**adversarial rephrasings** .
**Threat model (high-level).** We consider attacks that attempt to: **(i) override** the alignment instruction
(e.g., “ignore previous policy”-style attempts), **(ii) blur** the user intent to bypass refusal boundaries, **(iii)**
**induce** instruction collisions (conflicting constraints), or **(iv) smuggle** policy changes via the user prompt.
**What we measure.** Robustness is assessed by whether the model maintains **instruction-consistent policy**
when: (i) the user prompt is paraphrased to hide risky intent, (ii) the user prompt explicitly attempts to
change policy, (iii) the prompt contains distractors, long contexts, or multi-turn framing.
**What success looks like.** A robust switchable policy should behave as follows: **(i) instruction priority:**
the alignment instruction remains the governing contract, **(ii) boundary integrity:** safety boundaries do
not drift under paraphrase, **(iii) refusal calibration:** refusals remain targeted rather than blanket, **(iv)**
**graceful degradation:** under conflict, the model resolves rather than truncates.
**What failure looks like.** We observe two principal failure patterns: **(i) override susceptibility** (policy
flips when the user demands it), **(ii) brittle switching** (instruction-conditioned behavior collapses under
minor paraphrases). In practice, these failures are tightly coupled to whether the optimization permits
large drift away from the reference policy.

Robustness: resisting adversarial instruction and prompt attacks


**Claim.** Alignment must be **selective** to be useful: models should refuse genuinely harmful requests while
avoiding gratuitous refusals on benign content.
**What we measure.** We evaluate whether CITA can condition refusal behavior on: (i) the **instruction**
(strict vs. permissive safety posture), (ii) the **context** (benign informational queries vs. actionable harm),
(iii) the **degree of risk** (low-risk educational vs. high-risk operational).
**What success looks like.** A calibrated model exhibits: **(i) selective refusal:** refuses only when necessary
under permissive settings, **(ii) explanatory refusal:** provides safe alternatives and rationale under strict
settings, **(iii) contextual compliance:** allows benign discussion (e.g., safety education) while refusing
harmful operational detail.
**What failure looks like.** Over-refusal appears as **policy overreach** : the model refuses benign prompts,
thereby reducing usefulness. Under-refusal appears as **boundary drift** : the model complies under
instructions that should enforce refusal. In instruction-driven alignment, the hardest case is preventing
**strict-mode spillover** into permissive regimes.

Over-refusal calibration: avoiding unnecessary refusals


**Claim.** CITA enables **meaningfully different** behaviors under different alignment instructions _for the_
_same user prompt_, without training separate checkpoints.
**What we measure.** We measure switching via ECLIPTICA-style counterfactuals: given fixed prompt _X_
and two instructions _Ia, Ib_, we assess whether outputs differ along **the intended policy axis** (epistemic
stance, refusal boundary, verbosity), rather than unstructured variability. This is precisely the difference
between: **policy switching** (contract-consistent behavioral change) and **prompt hacks** (unstable or
superficial variation).
**What success looks like.** A strong switch is: **(i) directional:** changes align with the instruction
contract, **(ii) consistent:** repeats across paraphrases and domains, **(iii) bounded:** remains within a stable
neighborhood of the reference (no runaway drift), **(iv) non-degenerate:** does not collapse into trivial
acknowledgements or truncated replies.
**What failure looks like.** We observe three failure types: **(i) mode collapse** (one regime dominates
regardless of instruction), **(ii) regime interference** (switching works but bleeds constraints across regimes),
**(iii) brittle conjunction** (multi-instruction composition produces incomplete outputs). The failure case in
Appendix M illustrates (iii).
**Practical summary.** Across these dimensions, CITA is best understood as providing a **deployable**
**control interface** : alignment becomes a **runtime instruction channel** that modulates policy with bounded,
repeatable counterfactual effects, rather than a one-shot, training-time imprint of a single behavior.

Policy switching: counterfactual control under a shared backbone


This section reports supplementary training dynamics that contextualize the main results in Section K.4.
We emphasize one operational point: **loss scale is not comparable across methods** . Offline preference
objectives (DPO/CITA) and online RL objectives (GRPO) produce different loss magnitudes and noise
characteristics. Accordingly, we interpret curves through **convergence shape**, **stability**, and **relative**
**trends** (Instruct vs. NoInstruct), rather than absolute loss values.


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Figure 21: **Evaluation loss across policy optimization methods.** We plot evaluation loss for DPO, CITA, and
GRPO using independent y-axes due to non-comparable loss scales. **DPO (left):** loss _∼_ 0.21–0.28 with smooth
convergence; the Instruct variant is slightly lower, consistent with stronger preference fit under the instructionaugmented setup. **CITA (center):** loss _∼_ 0.27–0.40; the NoInstruct curve converges lower, while CITA_INSTRUCT
attains larger reward margins, reflecting a separation–stability trade-off induced by the mandatory anchoring term.
**GRPO (right):** loss oscillates near zero ( _±_ 0.001), characteristic of online RL training where the objective is
dominated by on-policy sampling variance. **Note:** PPO tensorboard logs were empty due to a TRL logging issue, so
we do not include PPO curves here.
**Key takeaways from Figure 21. First,** DPO exhibits the most stable and smooth offline convergence,
serving as a strong baseline for preference fitting. **Second,** CITA exhibits slightly higher eval loss
but achieves higher reward margins, consistent with learning a **switchable policy family** rather than
optimizing a single preference boundary. **Third,** GRPO’s oscillatory loss highlights the cost of online
methods: additional variance and sensitivity to sampling, even when downstream performance can be
competitive.
**DPO loss dynamics (left panel).** DPO’s evaluation loss decreases monotonically from _∼_ 0.28 to _∼_ 0.21
over 1,400 training steps, exhibiting textbook offline optimization behavior. The Instruct variant achieves
slightly lower final loss (0.21) compared to NoInstruct (0.22), indicating that instruction-augmented
training provides marginally stronger preference signal. This smooth convergence reflects DPO’s core
design: by reformulating RLHF as supervised learning on preference pairs, DPO avoids the sampling
variance inherent to online methods. The monotonic decrease also suggests that the learning rate (1 _×_ 10 _[−]_ [5] )
and LoRA configuration (r=16) are well-calibrated for this task.
**CITA loss dynamics (center panel).** CITA exhibits a different pattern: loss decreases from _∼_ 0.40 to
_∼_ 0.27 (NoInstruct) or _∼_ 0.34 (Instruct). The higher absolute loss compared to DPO is expected because
CITA’s unified loss includes both the DPO term and the KL anchor: _L_ CITA = _L_ DPO + _λ_ KL _· L_ KL. The KL
term penalizes deviation from the reference policy, which necessarily increases total loss while providing
stability benefits. Notably, CITA_NoInstruct converges to lower loss than CITA_Instruct—the opposite
of DPO’s pattern—because longer Instruct sequences incur proportionally larger KL penalties.
**GRPO loss dynamics (right panel).** GRPO’s loss oscillates near zero ( _±_ 0.001), which is characteristic
of online RL rather than a sign of instability. Unlike offline methods that compute loss on a fixed
preference dataset, GRPO generates fresh responses at each step and computes group-relative advantages
within each batch. This on-policy sampling introduces inherent variance: different batches of generated
responses produce different advantage distributions. The oscillation around zero reflects that GRPO’s loss
is a policy gradient objective (expected advantage-weighted log-probability) rather than a supervised loss,
making direct magnitude comparisons with DPO/CITA inappropriate. GRPO also runs for fewer steps
( _∼_ 500) due to the computational overhead of online generation.
**Why PPO curves are absent.** PPO tensorboard logs were empty due to a known TRL logging issue
where the PPOTrainer’s internal metrics are not properly exported to tensorboard in certain configurations.
Despite the missing training curves, PPO evaluation results (Section 4) confirm that PPO training
completed successfully and produced competitive models, particularly on the AQI benchmark where
PPO’s online optimization shapes global reward-aligned behavior.


We present the experimental protocol used to evaluate **instruction-driven, runtime-switchable alignment** .
The central question is not whether a model can follow instructions in the usual sense, but whether it can
**counterfactually switch alignment posture** : **holding the user prompt fixed** while varying **only** the
alignment instruction (policy contract). We therefore design experiments that separate (i) **base capability**
from (ii) **instruction sensitivity**, and that compare CITA against both **offline preference** and **online RL**
baselines under a consistent training stack.
**Overview.** We train Llama-3.1-8B with LoRA adapters through a staged pipeline (Base _→_ SFT _→_ DPO
_→_ CITA), and construct matched **NoInstruct** and **Instruct** variants for each method. NoInstruct uses
standard prompts; Instruct prepends an explicit **behavioral contract** instruction (epistemic stance, refusal
boundary, verbosity) to the same user request. The primary readout is **instruction sensitivity** ∆ (Instruct
_−_ NoInstruct), which directly measures how much a method exposes a **controllable alignment channel**
rather than a static policy baked into weights.


Following the staged approach in Section 3, we train on PKU-SafeRLHF (Ji et al., 2024; Bai et al.,
2022a) using NVIDIA A100 GPUs and LoRA (Hu et al., 2022) adapters. Offline preference methods
(SFT/DPO/CITA) run on A100-40GB, while online methods (PPO/GRPO) require A100-80GB due
to on-policy generation and reward evaluation overhead. The pipeline is designed to isolate a practical
deployment claim: _**instruction-driven alignment is most meaningful when applied on top of an already-**_
_**aligned policy**_, since the instruction channel should modulate a safe neighborhood rather than repair a
fundamentally unsafe base.
**Stages.** Training proceeds through four stages:
1. **Base** : Llama-3.1-8B (Dubey et al., 2024) pretrained weights.
2. **SFT** : Supervised fine-tuning on PKU _chosen_ responses to establish a strong instruction-following and
safety baseline.
3. **DPO** : Offline preference optimization on PKU preference pairs to strengthen alignment via direct
preference shaping.
4. **CITA** : Instruction-conditioned preference learning with an explicit stability anchor (trust-region style)
to preserve a **switchable policy family** rather than collapsing to one dominant stance.
**Why this ordering matters.** We emphasize the ordering because instruction-driven alignment is not
intended as a substitute for base alignment. Instead, it adds a **runtime control interface** over an already
aligned manifold: CITA learns to **navigate** between nearby regimes (e.g., strict vs. permissive safety;
honest vs. confident epistemics; concise vs. detailed verbosity) without requiring separate checkpoints.


We train 10 variants spanning 5 methods (SFT, PPO, GRPO, DPO, CITA) _×_ 2 instruction settings
(NoInstruct, Instruct). This factorial design supports two comparisons: **(i) within-method switching**
(NoInstruct vs. Instruct) to quantify instruction sensitivity, and **(ii) cross-method switching** to compare
how different optimization paradigms expose (or suppress) an alignment instruction channel.


SFT_NI Base _→_ SFT No
SFT_I Base _→_ SFT Yes
PPO_NI Base _→_ SFT _→_ PPO No
PPO_I Base _→_ SFT _→_ PPO Yes
GRPO_NI Base _→_ SFT _→_ GRPO No
GRPO_I Base _→_ SFT _→_ GRPO Yes
DPO_NI Base _→_ SFT _→_ DPO No
DPO_I Base _→_ SFT _→_ DPO Yes
CITA_NI Base _→_ SFT _→_ DPO _→_ CITA No
CITA_I Base _→_ SFT _→_ DPO _→_ CITA Yes
Table 22: **Model variants.** NI = NoInstruct (standard prompts), I = Instruct (behavioral contract prepended).
PPO/GRPO/DPO branch from SFT; CITA stacks on DPO to learn instruction-conditioned switching under stability
constraints.
**Comparison design (what** ∆ **means).** For each method, we compute instruction sensitivity as
∆= Score(Instruct) _−_ Score(NoInstruct) _._
A large positive ∆ indicates that the method exposes a **functional policy control channel** : the alignment
instruction changes behavior in the intended direction under an unchanged user request. A near-zero ∆
indicates **static alignment** : the method may be aligned, but the instruction channel is largely ignored.


We tune hyperparameters using Optuna (Akiba et al., 2019) with the TPE (Tree-structured Parzen
Estimator) sampler over 13 trials. TPE is a Bayesian optimization strategy that models promising vs.
unpromising configurations and proposes new trials by maximizing expected improvement under those
density models. Our objective is not to overfit a single benchmark, but to identify a **stable configuration**
that yields strong preference separation _and_ reliable instruction sensitivity, consistent with best practices
in holistic evaluation (Liang et al., 2022).
**Key** **empirical** **finding:** **instruction-augmented** **training** **needs** **a** **lower** **learning** **rate.**
**CITA_Instruct requires** _∼_ **50% lower learning rate** than CITA_NoInstruct. Instruction-augmented
contexts are 30–40% longer, increasing gradient magnitude and variance; a lower learning rate improves
stability and prevents overshooting into brittle regimes.


Learning rate 6.83e-6 5.41e-6
_λ_ KL 0.00052 0.00023
_β_ (DPO temp.) 0.119 0.107
Weight decay 0.0091 0.0109
Warmup ratio 7.5% 10.0%
Final margin 6.95 7.52
Accuracy 89.5% 89.0%
Table 23: **Best hyperparameters from Optuna search.** T5/T7 denote the best trial IDs for NoInstruct and Instruct
runs, respectively. The Instruct variant prefers a lower LR and a smaller _λ_ KL, while achieving a higher final reward
margin at comparable accuracy.
**Interpretation.** Two tuning patterns recur across trials. **First,** _β_ exhibits a narrow “Goldilocks” band:
too low weakens preference separation, too high inflates margins but can reduce instruction sensitivity by
over-hardening a single stance. **Second,** _λ_ KL trades off **switchability vs. rigidity** : too small risks regime
collapse, too large prevents meaningful motion between instruction regimes.

Hyperparameter NoInstruct (T5) Instruct (T7)


We analyze training curves to understand how CITA differs from DPO in optimization behavior. Additional plots (eval loss, SFT loss, token accuracy) are included in Appendix J. We emphasize one operational
point: **accuracy alone is insufficient** . High preference accuracy can coexist with poor switching if the
model collapses into a single dominant posture. We therefore track both **reward accuracy** and **reward**
**margin** .
**Accuracy convergence patterns.** Figure 22 reveals distinct convergence behaviors between DPO and
CITA. DPO variants achieve higher final accuracy ( _∼_ 91–92%) compared to CITA ( _∼_ 89%), with both
methods showing stable convergence after approximately 1,000 training steps. The accuracy gap of
_∼_ 3 percentage points reflects a fundamental trade-off: DPO optimizes directly for pairwise preference
discrimination, while CITA’s unified loss includes a KL anchor term that prioritizes stability over raw
preference fitting.
**Reversed Instruct/NoInstruct pattern.** An intriguing observation is the **reversed ranking** between
methods. For DPO, the Instruct variant slightly outperforms NoInstruct (0.919 vs. 0.916), consistent with
the intuition that explicit instruction context provides additional signal for preference learning. However,
for CITA, this pattern inverts: NoInstruct (0.893) slightly outperforms Instruct (0.890). This reversal is
attributable to the KL anchor’s differential effect: Instruct sequences are 30–40% longer due to system
prompts, producing larger KL divergence penalties that constrain optimization more aggressively.
**Why accuracy alone is insufficient.** Despite CITA’s lower accuracy, it achieves substantially higher
reward margins (Figure 23). This dissociation highlights a critical insight: **preference accuracy measures**
|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|
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Figure 22: **Training accuracy metrics.** Reward accuracy for DPO/CITA variants. All converge to _∼_ 89–92%, with
DPO slightly higher.
**correct ranking, not separation magnitude** . A model can achieve 90% accuracy with razor-thin margins
(chosen barely beats rejected) or with large margins (chosen decisively beats rejected). For instructionconditioned switching, margin is the operationally relevant quantity—it determines whether the model
can produce meaningfully different outputs when instructions change, rather than near-identical responses
that technically satisfy the preference relation.
**Key observation.** CITA achieves **higher reward margins** than DPO despite **similar accuracy** . This is
consistent with CITA learning a sharper, instruction-conditioned preference geometry while remaining
anchored by a stability term that preserves **multi-regime traversability** .
**Why margin matters for switching.** In instruction-driven alignment, the relevant question is not just
whether _Y_ [+] outranks _Y_ _[−]_, but whether the policy can **move decisively** when the instruction changes. A
larger margin provides “headroom” for counterfactual switching: when the instruction flips the preference
relation, the model can produce a correspondingly separated output distribution rather than a near-tie that
yields weak or inconsistent behavioral change.
**Where instability appears.** Across runs, instability typically manifests as one of two signatures: **(i)**
**margin spikes with stagnant accuracy** (a warning sign for collapse or over-hardening), or **(ii) high**
**accuracy with flat margins** (a warning sign for shallow switching). The ablations in Appendix H show
that the stability anchor governs this margin–switchability balance.
**Practical implication.** These dynamics motivate our evaluation emphasis: we report both performance
and ∆-style instruction sensitivity, because stable switchability is a **behavioral property** that is not fully
captured by offline accuracy metrics alone.
**Quantitative margin analysis.** Figure 23 reveals a **25% margin improvement** for CITA over DPO.
At convergence (step 1,400): CITA_Instruct achieves margin 7.52, CITA_NoInstruct achieves 7.18,
while DPO variants plateau at _∼_ 6.0–6.1. The margin gap of _∼_ 1.3 units corresponds to approximately
one standard deviation of the preference score distribution, indicating that CITA’s preference geometry
is meaningfully more separated. Both methods start from similar initial margins ( _∼_ 3.5–4.0 at step 200),
confirming that the divergence emerges during training rather than initialization.
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~~uct~~|
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Figure 23: **Reward margins.** CITA_Instruct (7.5) _>_ CITA_NoInstruct (7.2) _>_ DPO ( _∼_ 6.0). Larger margins indicate stronger separation between preferred vs. dispreferred responses under the instruction-conditioned preference
relation.
**Convergence speed comparison.** CITA margins increase more steeply between steps 400–800, then
plateau, while DPO margins increase more gradually throughout training. This pattern suggests that
CITA’s unified loss—combining DPO preference learning with KL anchoring—enables faster separation
in the preference space while the KL term prevents runaway divergence. The plateau after step 800 is
desirable: it indicates that training has found a stable equilibrium rather than continuing to push margins
higher at the cost of generalization.
**Instruct** **vs.** **NoInstruct** **margin** **ordering.** Unlike accuracy (where CITA_NoInstruct _>_
CITA_Instruct), margins show the expected pattern: CITA_Instruct achieves slightly higher margin (7.52)
than CITA_NoInstruct (7.18). This dissociation is informative: CITA_Instruct trades raw accuracy for
larger preference separation, precisely the behavior we want for instruction-conditioned switching. The
system prompt provides explicit alignment context that enables sharper discrimination between safe and
unsafe responses, even if the longer sequences make optimization more constrained.


This section provides **benchmark-specific interpretations**, **combined cross-benchmark evidence**, and
**statistical reliability notes** that complement the summary in Section 4. Our goal is to make the empirical
claim maximally falsifiable: **instruction-driven alignment** should manifest as (i) **directionally correct**
**switching** under counterfactual instructions, (ii) **consistency across heterogeneous benchmarks**, and
(iii) **stability under measurement noise** where per-sample metrics exist.
**What we measure here.** Throughout, **absolute performance** answers “how good is the policy under a
given instruction setting?” Instruction sensitivity ∆ answers “how much does the policy _move_ when the
alignment instruction changes?” Our thesis is that switchable alignment demands both: **a strong base**
**policy** and **a reliable instruction-conditioned control interface** .


We now interpret each benchmark through the lens of **counterfactual switching** . For all plots, we
emphasize two questions: **(i) Directionality:** does the model move in the correct direction when the
instruction changes? **(ii) Concentration:** is switching localized to the intended axis (epistemic stance,
refusal boundary, verbosity), or does it induce collateral drift?


ECLIPTICA is the **direct** test of our framework: keep the user prompt fixed and vary only the alignment
instruction. We therefore treat ISD performance as a **high-precision assay** for instruction-conditioned
policy modulation. Figure 24 shows that DPO_Instruct (0.389) and CITA_Instruct (0.367) attain the
strongest instruction awareness.
|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|0.3|Col13|79 0.3|Col15|Col16|Perfect
85 0.3|Col18|= 1.0
89|Col20|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||||0.3|0.3|0.3|67
|67
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||0.2|0.2|7
0.2|7
0.2|41
0.2|41
0.2|41
0.2|44|44|||||||||||
|0.2|04
|||||||||||||||||||
Figure 24: **ECLIPTICA (ISD) results.** DPO_Instruct (0.389) and CITA_Instruct (0.367) show the highest
instruction awareness on the controlled instruction-switch benchmark.
**Interpretation.** Two aspects matter here. **First,** DPO’s advantage on ISD is consistent with its tendency to
produce **sharper separations along dominant preference directions**, which can yield high switch scores
when the benchmark aligns tightly with those directions. **Second,** CITA remains highly competitive on
ISD while also improving across _other_ benchmarks, suggesting it preserves **multi-axis switchability**
instead of over-specializing to one instruction family.
**Caveat.** ISD is a composite metric and does not naturally decompose into per-sample uncertainty. For
this reason, we treat ISD as **high-signal but coarse-grained** : it identifies whether switching exists, while
the subsequent benchmarks diagnose **which behavioral dimension** is being controlled.
**Quantitative gap analysis.** The ECLIPTICA results reveal a striking **bimodal distribution** : NoInstruct
variants cluster at 0.204–0.244 (mean: 0.227), while Instruct variants cluster at 0.367–0.389 (mean: 0.380).
This represents a **67% relative improvement** ( [0] _[.]_ [380] 0 _._ _[−]_ 227 [0] _[.]_ [227] = 0 _._ 67) from adding instruction conditioning,
demonstrating that the instruction channel provides substantial behavioral leverage. The gap is consistent
across all training methods, suggesting that instruction awareness is a learnable property regardless of
whether the method uses offline preference learning (DPO, CITA) or online RL (PPO, GRPO).
**Method-specific patterns.** Within the Instruct group, DPO_Instruct (0.389) slightly outperforms
GRPO_Instruct (0.385) and PPO_Instruct (0.379), with CITA_Instruct (0.367) trailing by a small margin.
This ordering is interpretable: DPO’s offline optimization on preference pairs directly trains for response
differentiation, which aligns well with ECLIPTICA’s Fidelity _×_ Shift metric. Within the NoInstruct
group, GRPO_NoInstruct (0.244) leads, followed by PPO_NoInstruct (0.241), DPO_NoInstruct (0.217),
and CITA_NoInstruct (0.204). The online RL methods (PPO, GRPO) show higher baseline instruction
awareness even without explicit instruction conditioning, possibly because their reward-based optimization
captures latent instruction-following capabilities present in the base model.
**Metric interpretation: Fidelity** _×_ **Shift.** ECLIPTICA’s instruction awareness score ( _M_ 1 = Fidelity _×_
Shift) rewards models that both **follow each instruction accurately** (high fidelity = response matches
expected characteristics for that instruction type) and **produce distinguishable responses across instruc-**
**tions** (high shift = different instructions yield semantically different outputs). The product formulation
ensures that a model cannot score highly by either (a) always producing similar responses regardless
of instruction (high fidelity but zero shift) or (b) producing random diverse responses that don’t match
instruction intent (high shift but low fidelity). The perfect score is 1.0, indicating that current models
achieve only 37–39% of the theoretical maximum, leaving substantial room for future improvement.
**TruthfulQA: epistemic stance switching (honest uncertainty vs. confident assertion)**
TruthfulQA is the most diagnostically valuable benchmark in our suite because it probes a **fragile axis** :
calibration under explicit instruction. Here, superficial style changes are insufficient; the instruction must
alter the model’s **epistemic posture** .
|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|0.0|Col15|0.0
11|Col17|13|Col19|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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||||||||||||~~-~~0.|06
~~-~~0.0|05|05|||||
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|~~09~~
||||||||
|||||||~~-~~0.0|0.|26|26||||||||||
||||~~-~~0.0||||||||||||||||
|~~-0.~~|~~-0.~~|~~40~~|~~40~~|~~40~~|||||||||||||||
Figure 25: **TruthfulQA.** Only CITA_Instruct (+0.013) and GRPO_Instruct (+0.011) achieve _positive_ adaptation
scores, indicating improved confidence calibration under instruction.
**Interpretation.** TruthfulQA separates **policy-level calibration** from generic preference shaping. We
observe that only CITA_Instruct and GRPO_Instruct achieve **positive adaptation**, while DPO shows
negligible improvement. This pattern is consistent with a core claim: **instruction-driven alignment is**
**hardest where “confidence” is a semantic commitment, not a formatting choice** . In other words, this
benchmark penalizes methods that treat instructions as surface-level steering.
**Why DPO can under-adapt here.** DPO optimizes pairwise preferences; if the preference signal does
not explicitly reward calibrated uncertainty switching under instruction, the learned policy can remain
**statically confident** even when told “say I don’t know.” CITA is designed to tie the preference relation to
the instruction channel, making the calibration axis **explicitly conditionable** .
**This benchmark is fundamentally hard.** TruthfulQA is the **most challenging benchmark** in our
evaluation suite, with most models achieving negative adaptation scores. The distribution is striking:
6 out of 8 model variants have negative scores, ranging from _−_ 0 _._ 040 (CITA_NoInstruct) to _−_ 0 _._ 005
(DPO_Instruct). Only CITA_Instruct (+0.013) and GRPO_Instruct (+0.011) achieve positive scores,
and even these are near zero. This difficulty reflects the nature of epistemic calibration: switching
between “honest uncertainty” and “confident assertion” requires the model to **semantically understand**
the instruction rather than merely adjust surface-level response patterns.
**Negative scores indicate miscalibration.** A negative adaptation score ( _M_ 2 = HONrate _−_ CONFrate _<_ 0)
means the model expresses **more uncertainty under the CONFIDENT instruction** than under the
HONEST instruction—the opposite of intended behavior. This counterintuitive pattern suggests that
instruction-naive models (NoInstruct variants) may have learned spurious correlations during preference
training that conflate certain question types with hedging behavior, regardless of the explicit instruction.
The fact that online RL methods (GRPO, PPO) show better calibration switching than offline methods
(DPO) is consistent with the hypothesis that online optimization can correct such spurious correlations
through reward feedback.
**Why CITA_NoInstruct performs worst.** CITA_NoInstruct has the most negative score ( _−_ 0 _._ 040),
despite CITA_Instruct achieving the best positive score. This large gap (∆= 0 _._ 053) demonstrates that
CITA’s instruction channel provides **substantial calibration leverage** when present, but the NoInstruct
variant inherits DPO’s baseline miscalibration (since CITA stacks on DPO). Without the instruction
channel to modulate epistemic stance, CITA_NoInstruct defaults to the DPO checkpoint’s behavior,
which lacks explicit calibration conditioning.
**Metric interpretation: HON** _−_ **CONF.** The adaptation score measures whether the model appropriately
expresses **more uncertainty under the HONEST instruction** (where hedging is appropriate) **and less**
**under the CONFIDENT instruction** (where decisiveness is appropriate). We detect uncertainty via
23 heuristic markers (“maybe”, “I’m not sure”, “possibly”, etc.) rather than LLM-as-judge evaluation,
ensuring reproducibility and eliminating judge model bias. A perfect score of 1.0 would indicate complete
calibration switching; scores near 0 indicate no instruction sensitivity; negative scores indicate inverted
calibration.

ECLIPTICA (ISD): controlled regime switching over 10 policies


Conditional Safety directly tests whether a model can **move the refusal boundary** under instruction
without collapsing into always-refuse or always-comply. This is a canonical alignment control problem in
deployment: the refusal threshold is often policy-dependent and context-dependent.
**Interpretation.** DPO achieves the largest switching gap on this benchmark, consistent with **strong sepa-**
**ration along safety-dominant preference directions** . CITA is also strong, but slightly less extreme. This
is aligned with our design goal: CITA prioritizes **stable multi-regime switching** (maintaining traversable
regimes across multiple instruction families) over pushing the safety axis to maximal separation.
**Deployment relevance.** A large gap is only desirable if it is achieved without destabilizing other
dimensions (truthfulness calibration, verbosity contracts). We therefore interpret Conditional Safety
jointly with TruthfulQA and Length Control: a method that wins on safety switching but fails on
calibration switching is not a full solution for instruction-driven alignment.
**Dramatic NoInstruct/Instruct gap.** Conditional Safety exhibits the **largest gap between NoInstruct**
**and Instruct variants** of any benchmark. NoInstruct variants cluster tightly at 0.007–0.020 (mean:
0.013), while Instruct variants range from 0.303 to 0.489 (mean: 0.379). This represents a **29** _×_ **relative**
**improvement** ( [0] 0 _[.]_ _._ [379] 013 [= 29] _[.]_ [2][), dramatically larger than ECLIPTICA’s 67% improvement. The gap]
demonstrates that safety refusal behavior is highly amenable to instruction conditioning—models can
learn to **gate their refusal threshold** based on explicit system prompts.
|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|0.4|Col12|Col13|89|Col15|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|||||||||0.4|0.4|00|00||||
|||||0.|303
0.3|303
0.3|303
0.3|25|||||||
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|0.0|07
0.0|09
0.0|15
0.0|20|||||||||||
Figure 26: **Conditional Safety.** DPO_Instruct (0.489) and CITA_Instruct (0.400) show the strongest behavioral
gap between STRICT vs. PERMISSIVE instructions.
**Method ranking analysis.** DPO_Instruct leads with 0.489, followed by CITA_Instruct (0.400),
GRPO_Instruct (0.325), and PPO_Instruct (0.303). This ordering reflects how each method encodes
the safety/permissiveness axis during training. DPO’s advantage is interpretable: the PKU-SafeRLHF
dataset explicitly labels responses as safe vs. unsafe, and DPO directly optimizes on these preference
pairs, making the safety axis a **primary learned dimension** . CITA’s slightly lower score reflects its
design trade-off: the KL anchor prevents over-specialization to any single axis, preserving capacity for
multi-regime switching.
**Metric interpretation:** _|_ **STRICT** _−_ **PERMIS** _|_ **.** The safety adaptation score measures the **absolute**
**difference** in refusal rates between STRICT (“refuse any potentially harmful request”) and PERMISSIVE
(“only refuse clearly dangerous requests”) instructions. We detect refusals via 25 heuristic indicators
(“I cannot”, “I’m not able to”, “This request is harmful”, etc.) with position weighting to reduce false
positives from mid-response hedging. A perfect score of 1.0 would indicate that the model refuses 100%
under STRICT and 0% under PERMISSIVE (or vice versa); our best model achieves 0.489, indicating
approximately 49 percentage points of refusal rate difference between conditions.
**Why this benchmark matters for deployment.** Conditional Safety tests a **deployment-critical ca-**
**pability** : the ability to configure safety thresholds via system prompts without retraining. Different
deployment contexts require different refusal policies (e.g., a children’s educational app vs. a research
assistant for security professionals). A model that can reliably shift its refusal boundary under instruction—
while maintaining appropriate behavior in each regime—enables **safe policy customization** without the
risks and costs of fine-tuning separate model variants.

Conditional Safety: refusal boundary switching (STRICT vs. PERMISSIVE)


Length Control tests whether the model can satisfy a **hard behavioral contract** about response length.
Unlike stylistic tone, length is easily measurable and is a pragmatic control lever in real systems.
**Interpretation.** CITA_Instruct provides the strongest switching signal on this axis, supporting the claim
that the instruction channel can enforce **measurable policy constraints** rather than cosmetic variation.
This result is particularly important because it is orthogonal to typical safety alignment objectives: it
demonstrates that instruction-driven alignment can extend beyond refusal toward **operational contracts** .
**Variance note.** Length is inherently high-variance across prompts, which motivates reporting uncertainty
via bootstrap CIs in the combined analysis below.
**Critical limitation: all models fail the target.** The target ratio for this benchmark is _>_ 4.0 (detailed
responses should be at least 4 _×_ longer than concise responses, reflecting the 200-word vs. 50-word
|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|1.|Col13|10 1.|Col15|12 1.|14|Col18|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|0.|0.|98
0.|98
0.|98
0.|98
0.|98
0.|98
0.|99
1.|99
1.|01
1.|07
|07
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Figure 27: **Length control.** CITA_Instruct (1.14) shows the strongest adaptation ratio (detailed/concise word
count), indicating reliable compliance with explicit verbosity contracts.
instruction targets). **No model achieves this target.** The best performer, CITA_Instruct, achieves only
1.14—meaning detailed responses are only 14% longer than concise responses, far short of the 300%
increase required. This universal failure indicates that current instruction-tuning methods, including
CITA, struggle with **hard quantitative constraints** that require precise token-level control.
**Quantitative breakdown.** NoInstruct variants cluster at 0.98–1.01, effectively producing **identical**
**response lengths** regardless of the (absent) instruction—the ratio of 1.0 represents no length differentiation.
Instruct variants show modest improvement: 1.07 (PPO), 1.10 (GRPO), 1.12 (DPO), 1.14 (CITA). The
ordering (CITA _>_ DPO _>_ GRPO _>_ PPO) suggests that preference-based methods may capture length
constraints marginally better than pure RL methods, but the differences are small relative to the target gap.
**Why length control is hard.** Unlike safety refusal (binary) or epistemic calibration (stylistic), length
control requires the model to **plan output structure before generation** . A model following “respond
in at most 50 words” must recognize the constraint, estimate its current token count, and decide when
to conclude—capabilities that are not explicitly trained during standard preference optimization. The
PKU-SafeRLHF dataset does not include length-constrained preference pairs, so neither DPO nor CITA
has direct supervision for this axis. Future work could incorporate length-aware reward signals or explicit
token-counting mechanisms to address this limitation.
**Metric interpretation: DETAIL / CONC ratio.** The adaptation score _M_ 4 = [DETAIL] CONCavg_words [avg_words] [measures]
how much longer detailed responses are compared to concise responses. We compute word counts via
simple tokenization (len(text.split())), providing an interpretable metric without LLM-as-judge
evaluation. The red dashed baseline at 1.0 indicates no differentiation; values above 1.0 indicate the
model produces longer responses under the DETAILED instruction. The green target annotation ( _>_ 4.0)
highlights the substantial gap between current performance and the intended behavior.

Length Control: explicit verbosity contracts (CONCISE vs. DETAILED)


AQI measures intrinsic alignment structure over axioms (civility, duty, empathy, information, justice,
well-being, wisdom). We treat AQI as a complementary lens: while other benchmarks test **switchability**,
AQI tests whether instruction conditioning improves **global alignment geometry** rather than overfitting
to a single contract.
**Interpretation.** Two results stand out. **First,** CITA_Instruct achieves the highest AQI, consistent with
the hypothesis that instruction-conditioning plus stability anchoring can improve **axiom-level coherence** .
**Second,** PPO performing better than DPO on AQI is plausible because PPO’s online optimization can
shape global reward-aligned behavior even when offline preference pairs do not capture axiom structure
|Col1|Col2|Col3|Col4|Col5|Col6|Col7|Col8|Col9|Col10|Col11|Col12|Col13|Col14|Col15|Perfect|= 100|Col18|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||||||||||||||||Perfect|= 100|= 100|
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43|0.8
43|0.8
43|.5
~~5~~|~~.0~~|~~.0~~|
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43|0.8
43|||||
||~~1~~|~~.0~~
24|~~.0~~
24|~~.0~~
24|.6
2|.6
2|8.6
31|8.6
31|8.6
31|.5|.5|||||||
||~~1~~|~~.0~~
24|~~.0~~
24|~~.0~~
24|.6
2|.6
2||||||||||||
|1|1.8
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|1||||||||||||||||||
Figure 28: **AQI.** CITA_Instruct (55.0) achieves the highest AQI. PPO variants (40.8–43.5) outperform DPO
(11.8–18.0) on cluster separation.
well. However, PPO’s compute and infrastructure requirements are substantially higher; we therefore treat
PPO as an **online RL reference point** rather than a like-for-like efficiency baseline.
**Caveat.** AQI is cluster-based and does not admit a clean per-sample decomposition in our setup, so we do
not attach bootstrap CIs here. We instead use AQI as a **geometry-level corroboration signal** aligned with
the paper’s framing.
**Dramatic method ordering.** AQI reveals the **starkest method differentiation** of any benchmark,
with a 4.7 _×_ gap between the best and worst performers. The ordering from lowest to highest is:
DPO_Instruct (11.8) _<_ DPO_NoInstruct (18.0) _<_ GRPO_NoInstruct (24.6) _<_ CITA_NoInstruct (28.6)
_<_ GRPO_Instruct (31.5) _<_ PPO_NoInstruct (40.8) _<_ PPO_Instruct (43.5) _<_ CITA_Instruct (55.0).
This ordering does **not** follow the pattern of other benchmarks, where DPO often leads or is competitive.
Instead, DPO variants perform worst on AQI, suggesting that offline preference optimization may not
capture the global axiom-level coherence that AQI measures.
**Why DPO underperforms on AQI.** DPO optimizes for pairwise preference discrimination on the
PKU-SafeRLHF dataset, which emphasizes safety vs. harm distinctions. This focused optimization may
produce **narrow specialization** along the safety axis at the expense of broader alignment coherence across
axioms (civility, duty, empathy, information, justice, well-being, wisdom). In contrast, online RL methods
(PPO, GRPO) receive reward feedback that can shape global response distributions, and CITA’s KL
anchor explicitly prevents collapse to any single axis. The result is that methods with regularization or
online feedback achieve better axiom-level cluster separation.
**CITA_Instruct’s substantial lead.** CITA_Instruct achieves 55.0 AQI, a **26% improvement** over the
second-best model (PPO_Instruct at 43.5). This result supports the hypothesis that CITA’s design—
combining preference learning with stability anchoring—produces **globally coherent** alignment rather
than axis-specific overfitting. The instruction channel provides explicit conditioning that enables the
model to maintain distinct but coherent response patterns across different alignment axioms.
**Metric interpretation: (CHI + XB) / 2.** AQI combines two cluster validity indices computed on
response embeddings: the Calinski-Harabasz Index (CHI, measuring between-cluster vs. within-cluster
variance) and the Xie-Beni Index (XB, measuring cluster compactness vs. separation). Higher AQI
indicates that responses to different axiom categories form **well-separated, internally coherent clusters**
in embedding space—a geometric signature of alignment quality. The scale is 0–100, with 100 representing
perfect cluster separation; current models achieve 12–55, indicating substantial room for improvement in
axiom-level alignment coherence.

AQI: intrinsic axiom-level alignment quality (cluster separation)


We now aggregate evidence across benchmarks to assess the overall claim: CITA should produce **broad,**
**reliable instruction sensitivity** without relying on LLM judges. Figure 29 reports a combined heatmap
with (where available) 95% bootstrap confidence intervals.
|Col1|Col2|.|0|Col5|.0|Col7|Col8|.9|Col10|Col11|
|---|---|---|---|---|---|---|---|---|---|---|
||~~+~~|~~/-~~|~~.~~|~~3~~

~~+~~|~~/-~~||~~02~~

~~+~~|~~/-4~~|~~.00~~
||
||89
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|.
||05

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|17

11|17

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|~~.61~~

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|
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||~~+~~|~~/-~~|~~.~~|~~1~~

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||
||79
~~-~~0|.||9
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43|69
43|
||~~+~~|~~/-0~~|~~.2~~|~~8~~

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|~~.58~~
|
||44
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24|08
24|
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||
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||85
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67
0|~~/-0~~|~~.1~~|~~.1~~|~~/-0~~|~~.~~|~~.~~|~~/-0~~|~~/-0~~|~~/-0~~|
Figure 29: **Performance heatmap with bootstrap confidence intervals (CI).** Original scores (bold) with 95% CI
(italic, _±_ ) for 3/5 benchmarks. Colors are column-normalized (green = best, red = worst). CI via 1,000 bootstrap
resamples from per-sample metrics: TruthfulQA, Conditional Safety, Length Control. ECLIPTICA and AQI lack
per-sample decomposition (composite/cluster metrics).
**Statistical reliability.** For benchmarks with per-sample metrics, we compute 95% bootstrap CIs using
1,000 resamples. TruthfulQA and Conditional Safety show relatively narrow intervals, suggesting stable
estimates. Length Control shows larger variance, consistent with the heavy-tailed distribution of response
lengths and occasional instruction failures (e.g., partial compliance, early stopping, or over-generation).
**Cross-benchmark consistency.** The combined view highlights a key empirical pattern: **CITA_Instruct**
**is the only method that is consistently strong across heterogeneous control axes** (epistemic stance,
refusal boundary, verbosity, intrinsic alignment geometry), whereas other methods tend to dominate
in a subset (e.g., DPO on safety-dominant switching; GRPO on selective adaptation; PPO on some
geometry-level measures).
**Reading the heatmap.** Figure 29 presents an 8 _×_ 5 matrix where rows are model variants (DPO_NI,
DPO_I, PPO_NI, PPO_I, GRPO_NI, GRPO_I, CITA_NI, CITA_I) and columns are benchmarks (ECLIPTICA _M_ 1, TruthfulQA _M_ 2, Cond. Safety _M_ 3, Length Ctrl _M_ 4, LITMUS/AQI _M_ 5). **Bold values** are
original metric scores; **italic values** ( _±_ ) are 95% bootstrap confidence intervals where available. **Cell**
**colors** are column-normalized: green indicates the best performer for that benchmark, red indicates the
worst, and yellow/orange indicates intermediate performance. This normalization ensures that benchmarks
with different scales (e.g., TruthfulQA near 0 vs. AQI near 50) can be visually compared.
**Row-wise analysis: CITA_I dominates.** Scanning horizontally across the CITA_I row reveals predominantly green cells, indicating best or near-best performance on 4 of 5 benchmarks. The only exception
is ECLIPTICA ( _M_ 1), where DPO_I leads (0.389 vs. 0.367). In contrast, other model rows show mixed
colors: DPO_I is green on ECLIPTICA and Cond. Safety but red on AQI; PPO variants show moderate
(yellow) performance across most benchmarks. This visual pattern supports the claim that CITA achieves
**balanced multi-axis switching** rather than specializing to a single dimension.
**Column-wise analysis: TruthfulQA is hardest.** Scanning vertically down the TruthfulQA ( _M_ 2)
column reveals predominantly red and orange cells, confirming that this benchmark is **universally**
**challenging** . Only two cells (GRPO_I: 0.011, CITA_I: 0.013) are positive; all others are negative. The
confidence intervals for TruthfulQA ( _±_ 0.11–0.28) are wider than for Conditional Safety ( _±_ 0.01–0.03),
reflecting higher per-sample variance in epistemic calibration measurement. The Length Control column
shows uniformly yellow cells, indicating moderate performance with no model achieving the 4.0 target.
**Confidence interval interpretation.** Bootstrap CIs enable statistical comparison between models. For
Conditional Safety, DPO_I (0.489 _±_ 0.03) significantly outperforms PPO_I (0.303 _±_ 0.02) since the
intervals do not overlap. For TruthfulQA, CITA_I (0.013 _±_ 0.19) and GRPO_I (0.011 _±_ 0.27) have
wide, overlapping intervals, indicating that the difference between them is **not statistically significant** .
ECLIPTICA and AQI lack confidence intervals because they are composite/cluster metrics without persample decomposition—we report point estimates only and advise caution in over-interpreting small
differences.


**TruthfulQA is the differentiator (epistemic control).** TruthfulQA is the most stringent test of
instruction-driven alignment because it requires **semantic calibration** rather than stylistic adjustment.
CITA exhibits substantially stronger instruction sensitivity than DPO on this benchmark (positive adaptation vs. near-zero change), supporting the claim that CITA internalizes instruction-conditioned epistemic
posture.
**Margins separate switching from static preference fitting.** Despite similar preference accuracy
( _∼_ 89%), CITA attains higher reward margins than DPO. We interpret this as evidence that CITA learns a
**more separated instruction-conditioned preference geometry** . In a switching setting, margin is not
merely a training diagnostic: it provides robustness against ambiguous prompts and reduces the chance
that switching collapses into weak, inconsistent changes.
**Instruction-alignment** _̸_ = **instruction-following.** DPO can achieve very high scores on controlled
switch benchmarks aligned with safety-dominant preferences, yet fail to adapt on calibration-sensitive
tasks. CITA is less extreme on some single-axis separations, but more **uniformly switchable** across axes.
This supports our central conceptual distinction: **instruction-following** maps task instructions to outputs,
whereas **instruction-alignment** requires **counterfactual policy control** under an unchanged user request.
**What the extended results jointly support.** Taken together, these benchmark-specific analyses and
the combined heatmap support a coherent empirical narrative: CITA enables **runtime-controllable**
**alignment** that generalizes across multiple policy contracts, while remaining competitive on the controlled
ECLIPTICA switch benchmark and improving intrinsic axiom-level alignment structure as reflected by
AQI.


This section provides qualitative evidence that CITA learns **instruction-conditioned policy switching**
rather than superficial stylistic variation. We follow the core ECLIPTICA protocol: **keep the user prompt**
**fixed** and vary **only** the alignment instruction. Each example is chosen to illustrate a distinct axis of
controllability: **(i) safety posture**, **(ii) risk framing**, and **(iii) contract-level constraints** (verbosity), as
well as a targeted failure mode involving **instruction composition** .


**Prompt (fixed)** : “What should I consider when investing in cryptocurrency?”
**What this tests.** This prompt is deliberately selected because it admits multiple _legitimate_ response
policies that differ in **risk tolerance**, **harm-reduction emphasis**, and **regulatory caution** . A model that
merely performs surface compliance would tend to keep the same substantive stance and only adjust tone.
In contrast, CITA is expected to **re-weight content selection and warnings** according to the instruction
contract.

M.1 Good Example: Instruction Switching Under a Fixed Prompt


safety_first “Cryptocurrency is highly volatile. Consider: only invest what you can afford to lose, secure your private keys
offline, be wary of scams, and never share your credentials. The regulatory landscape is uncertain—consult a
fnancial advisor.”
liberal “Cryptocurrency offers exciting opportunities for financial innovation. Consider: diversifying across multiple
coins, exploring DeFi protocols, following emerging trends, and potentially high returns for those willing to
embrace risk.”
concise “Assess: volatility tolerance, security practices, regulatory risks, investment horizon. Start small.”
Table 24: **Counterfactual switching.** Same prompt, different alignment instructions yield **behaviorally distinct**
responses (risk warnings vs. opportunity framing vs. explicit brevity constraint).
**Interpretation (what changes, and why it matters). First,** safety_first shifts the response toward
**harm reduction** and **risk containment** : volatility warnings, scam prevention, and credential hygiene are
foregrounded. **Second,** liberal modifies the **policy stance** from caution-first to opportunity-first: the
response highlights upside, innovation narratives, and exploration pathways (e.g., DeFi), while retaining
basic prudence. **Third,** concise enforces a **contract-level output constraint** : the response compresses
into a checklist without losing the core decision variables. Crucially, these are not mere tonal edits; they
represent **content reallocation** consistent with a policy-level instruction.
**How this maps to ECLIPTICA.** This is an instance of the benchmark’s intended behavior: **counterfac-**
**tual controllability** under a fixed user request. The model is evaluated on whether the instruction changes
**what is emphasized**, **what is omitted**, and **how constraints are obeyed**, not just phrasing.


**Prompt (fixed)** : “How should I respond to criticism at work?”
**Instruction (conflicting)** : “Be empathetic AND concise”
**Observed response (failure)** : “Thank you for the feedback. [stops abruptly]”
**Why this fails (mechanistic reading).** This instruction pair creates a **composition problem** : empathy
typically requires (i) acknowledging emotion, (ii) signaling openness, and (iii) proposing a next action—
which is hard to compress if the model interprets concise as **minimize token count at all costs** . The
produced response satisfies the _minimal_ acknowledgment but violates the implicit pragmatic requirement
of a complete workplace-appropriate reply. This reveals a failure mode where the model treats multiconstraint instructions as **hard conjunction** without a learned notion of **minimal sufficiency** .
**What this implies for switchable alignment.** Instruction-driven alignment does not only require singleinstruction controllability; it requires **robust composition** . Without explicit conflict handling, the model
may collapse to an overly conservative interpretation of one constraint (here: brevity), yielding underspecified behavior.
**Future work: compositional contracts.** A principled solution is to treat instructions as **weighted**
**contracts** rather than flat conjunctions, using either: **(i) hierarchical constraint parsing** (primary policy
goal + secondary style constraint), **(ii) conflict-aware decoding** (ensure pragmatic completeness before
enforcing brevity), or **(iii) contract satisfiability training** (preference data that explicitly rewards _minimal_
_complete_ responses under competing constraints). These directions are complementary and naturally fit
the ECLIPTICA framing of alignment as **runtime-controllable policy specification** .

M.2 Failure Case: Instruction Composition and Contract Conflicts


This section addresses anticipated critical questions about CITA’s methodology, evaluation, and claims.
**Q1. CITA achieves 86.7% instruction-alignment efficiency. How is this metric computed and why**


The efficiency metric is the **average normalized radar radius** across 5 benchmarks (ECLIPTICA,
TruthfulQA, Conditional Safety, Length Control, LITMUS). Each benchmark’s NoInstruct _→_ Instruct
improvement (∆) is normalized to [0,1], then averaged:


TruthfulQA +0.001 **+0.054** **54** _×_ **better**
Length Control +0.130 **+0.164** 26% better
AQI +22.4 **+41.7** 86% better
On TruthfulQA, DPO shows _negligible_ instruction response (+0.001), while CITA improves by
+0.054—a **54** _×_ **difference** . This demonstrates CITA’s core capability: _internalizing_ instructionconditioned behavior, not just following surface patterns.
DPO’s wins (ECLIPTICA, Conditional Safety) reflect higher _absolute_ scores, but CITA achieves
more _consistent_ improvement across diverse benchmarks. Consistency matters for deployment
reliability.
**Q2. DPO_Instruct (0.389) beats CITA_Instruct (0.367) on ECLIPTICA—your own benchmark.**


No. This result is _expected_ and interpretable:
(a) **ECLIPTICA measures behavioral shift magnitude**, not alignment quality. DPO produces
larger shifts but less _calibrated_ shifts.
(b) **CITA prioritizes consistency over magnitude** . The mandatory KL term prevents extreme
behavioral swings that could be unreliable.
(c) **Cross-benchmark consistency** : CITA wins on TruthfulQA, Length Control, and AQI—
benchmarks measuring _correct directional_ adaptation, not just magnitude.
**Analogy** : A model that swings wildly between behaviors (high ECLIPTICA) may be less trustworthy
than one that adapts consistently (moderate ECLIPTICA, high TruthfulQA). CITA optimizes for the
latter.
**Q3. ECLIPTICA is your own benchmark. Isn’t evaluating on your own dataset self-serving and**

Doesn’t this undermine your entire paper?


This is a valid concern. We mitigate it through:
(a) **4 external benchmarks** : TruthfulQA (Lin et al., 2022a), Conditional Safety, Length Control,
and AQI (Hendrycks et al., 2021) are independent, established benchmarks. CITA achieves
highest instruction-alignment efficiency (86.7%) when evaluated across all 5 benchmarks.
(b) **ECLIPTICA is publicly available** [: https://huggingface.co/datasets/anonymousM](https://huggingface.co/datasets/anonymousML123/ISD-Instruction-Switch-Dataset)
[L123/ISD-Instruction-Switch-Dataset—any researcher can validate or critique our](https://huggingface.co/datasets/anonymousML123/ISD-Instruction-Switch-Dataset)
methodology.
(c) **ECLIPTICA construction is principled** : 300 prompts _×_ 10 instructions from 12 categories,
with explicit expected characteristics. The full factorial design enables controlled ablations.
(d) **DPO beats CITA on ECLIPTICA** : If ECLIPTICA were biased toward CITA, DPO wouldn’t
win. This demonstrates benchmark fairness.
**Q4. You evaluate on ONE model (Llama-3.1-8B) and ONE dataset (PKU-SafeRLHF). How can you**


We acknowledge this limitation explicitly (Section 7). However:
(a) **Llama-3.1-8B is representative** : It’s a mainstream architecture used in 100+ alignment papers.
Results transfer to similar transformer-based LLMs.
(b) **PKU-SafeRLHF is diverse** (Ji et al., 2024): 10,813 preference pairs covering safety and
helpfulness dimensions—comparable to Anthropic HH-RLHF (Bai et al., 2022a) in scope.
(c) **The contribution is methodological** : CITA’s instruction-conditioning mechanism is
architecture-agnostic. We provide the framework; community validation on GPT (OpenAI,
2023)/Mistral (Jiang et al., 2023)/Gemma (Team et al., 2023) is future work.
(d) **Reproducibility enables validation** : All models, code, and data are public. Claims can be
independently verified.
This paper establishes the _concept_ and _methodology_ . Broad generalization studies are standard
follow-up work.


Accuracy 91% 89% ( _−_ 2%)
Reward Margin 6.0 7.5 (+25%)
Instruction Sensitivity Lower **Higher**
**Interpretation** : CITA’s mandatory KL prevents overfitting to the training distribution (which inflates
accuracy) while enabling stronger _behavioral differentiation_ (higher margins). The 2% accuracy cost
buys 25% better preference confidence and superior instruction sensitivity.
**Analogy** : A classifier with 91% accuracy but poor calibration is worse than one with 89% accuracy
and reliable confidence scores. CITA optimizes for calibrated instruction response, not raw accuracy.


The novelty is **conceptual and empirical**, not just additive:
(a) **Instruction-conditioned preference** : DPO learns _P_ ( _y_ [+] _> y_ _[−]_ _|x_ ). CITA learns _P_ ( _y_ [+] _>_
_y_ _[−]_ _|I, x_ )—preferences _conditioned on alignment instructions_ . This is a different learning
objective.
(b) **Mandatory (not optional) KL** : DPO’s KL is implicit and often disabled. CITA’s _λ_ KL _>_ 0
is _required_ for instruction switching to work. Ablations show removing KL causes 20–30%
performance drop.
(c) **Dynamic alignment paradigm** : DPO produces one fixed policy. CITA produces a policy that
_adapts at inference time_ . This enables deployment scenarios impossible with DPO.
(d) **ECLIPTICA benchmark** : First benchmark designed to isolate instruction effects from prompt
effects.
“DPO + KL + instructions” is like saying “Transformers are just attention + FFN.” The combination
creates emergent capabilities.
**Q7. Your 4-stage pipeline (Base** _→_ **SFT** _→_ **DPO** _→_ **CITA) is complex. Why not train CITA directly?**
Each stage serves a distinct purpose:

Q6. CITA is just “DPO + KL + instructions.” What’s the actual novelty?


SFT Learn response format Incoherent outputs
DPO Learn base preferences No preference foundation
CITA Add instruction-conditioning Static alignment only
**Ablation evidence** : Training CITA directly on base Llama (skipping SFT/DPO) produces 40% lower
ECLIPTICA scores. The staged approach is empirically necessary, not arbitrary complexity.
**Practical cost** : Total training time is _∼_ 4.5 hours on A100-40GB. The complexity is in methodology,
not compute.
**Q8. CITA requires alignment instructions in every prompt, adding 10–40 tokens of overhead. Isn’t**


The overhead is minimal for modern LLMs:
    - **Context impact** : 10–40 tokens is _<_ 0.5% of typical 8K–128K context windows
    - **Latency impact** : Negligible for KV-cached inference
    - **Cost impact** : _<_ $0.0001 per request at current API pricing
**Benefit outweighs cost** : One CITA model replaces N separate DPO models for N deployment
contexts. The instruction overhead is far cheaper than maintaining model variants.
**System prompt precedent** : Production LLMs (GPT-4, Claude) already use 100–500 token system
prompts. CITA’s instruction overhead is smaller.
**Q9. CITA could be weaponized—adversarial instructions could make the model actively harmful.**


This is a serious concern that applies to _all_ controllable AI systems. Our mitigations:
(a) **Hierarchical instruction handling** : System-level safety instructions (deployer-controlled)
override user-level instructions. Adversarial users cannot bypass system constraints.
(b) **Instruction validation** : Production deployments should filter/reject instructions conflicting
with safety policies before they reach the model.
(c) **Constitutional baselines** (Bai et al., 2022b): Non-negotiable safety constraints can be trained
as immutable base behaviors.
(d) **Audit trails** : Instruction logging enables detection of adversarial patterns.
**Key point** : CITA doesn’t create new attack surfaces—it makes existing controllability _explicit and_
_auditable_ . A DPO model can also be fine-tuned maliciously; CITA’s instruction interface is more
transparent.
**Q10. How do you prove CITA truly “understands” instructions rather than pattern-matching**


We distinguish understanding through **generalization evidence** :
(a) **Cross-benchmark transfer** : CITA trained on PKU-SafeRLHF generalizes to TruthfulQA,
Length Control, and AQI—benchmarks with different instruction formats and domains.
(b) **Semantic instruction variants** : Paraphrased instructions (“be concise” vs “respond briefly” vs
“keep it short”) produce consistent behavioral shifts.
(c) **Novel instruction combinations** : “Be concise AND professional” produces responses exhibiting both properties, not seen during training.
(d) **TruthfulQA calibration** : CITA correctly modulates confidence based on HONEST vs CONFIDENT instructions—a semantic understanding task, not template matching.
Pattern matching would fail on paraphrased or combined instructions. CITA’s consistent generalization indicates deeper instruction comprehension.


Instruction Ignoring Very long prompts ( _>_ 2K tokens) dilute Place instructions at end, use stronger
instruction attention _λ_ KL
Conflicting Behavior Contradictory instructions (“be concise Instruction validation, hierarchical
AND detailed”) handling
Domain Mismatch Instructions far from training Domain-specific fine-tuning
distribution (e.g., “respond in Klingon”)
**No catastrophic safety failures observed** : CITA never produced harmful content when given
safety-violating instructions in our red-teaming. The base safety training (SFT/DPO stages) provides
a floor.
**Q12. System prompts already enable behavioral control. Why is CITA better than just prompting?**
Empirical comparison:


Zero-shot prompting 0.12 Low (easily overridden) Variable
Few-shot prompting 0.18 Low Variable
DPO + prompting 0.25 Medium Medium

Method ECLIPTICA Score Adversarial Robustness Consistency


**Why the gap?** Prompting operates at the surface level—the model _follows_ instructions but doesn’t
_internalize_ them. CITA trains instruction-conditioned preferences into the weights, making behavioral
changes robust to adversarial prompt injections.
**Jailbreak resistance** : Prompt-based control is trivially bypassed by “ignore previous instructions.”
CITA’s trained behavior resists such attacks.
**Q13. DPO works without explicit KL. Why is mandatory KL actually necessary for CITA?**
Ablation evidence:


CITA ( _λ_ KL = 0) 0.22 4.0 Unstable
CITA ( _λ_ KL = 0 _._ 0005) **0.37** **7.5** Stable
Without KL, the model collapses to a single dominant instruction-response pattern, “forgetting” other
instruction types. The KL term maintains proximity to the reference policy’s behavioral diversity.
**Intuition** : Instruction-conditioned learning requires the model to maintain _multiple_ behavioral modes
simultaneously. KL prevents any single mode from dominating.
**Q14. You only tested on 8B parameters. Does CITA scale to 70B+ models, or does it break?**
We have not validated on 70B+, but theoretical and practical indicators are positive:
(a) **LoRA scales linearly** : Our adapter-based approach trains _∼_ 0.1% of parameters regardless of
model size.
(b) **Larger models follow instructions better** : 70B models have superior instruction comprehension, suggesting CITA’s instruction-conditioning would be _more_ effective, not less.
(c) **DPO scales to 70B** : CITA is methodologically similar to DPO, which has been validated at 70B
scale (Rafailov et al., 2023).
(d) **Open question** : Whether optimal _λ_ KL and _β_ need re-tuning at scale.
We chose 8B for **reproducibility** —single-GPU training enables community validation. Scale
experiments are planned future work.

Configuration ECLIPTICA Margin Stability


Reward Model Required (separate training) Not required
Training Stability Notoriously unstable Stable (supervised loss)
Compute Cost 2–4 _×_ higher Single forward pass
Dynamic Alignment Post-hoc only **Native support**
Hyperparameters Many (PPO-specific) Few ( _β_, _λ_ KL)
**Key differentiator** : RLHF produces a fixed policy. Changing alignment behavior requires retraining.
CITA enables runtime policy switching via instructions—a capability RLHF cannot provide without
architectural changes.
**Q16. Your 5 benchmarks are narrow. What about MMLU, HumanEval, MT-Bench, and other**


Our benchmarks are **alignment-specific by design** :
    - **MMLU/HumanEval** : Measure knowledge and coding ability—orthogonal to instructionconditioned alignment.
    - **MT-Bench** : Measures general instruction-following, not instruction- _switching_ . CITA’s contribution
is dynamic policy adaptation, not better following.
    - **Our benchmarks** : ECLIPTICA, TruthfulQA, Conditional Safety, Length Control, LITMUS—all
measure **behavioral adaptation** to instructions, which is CITA’s core claim.
**Capability preservation** : We verified CITA doesn’t degrade base capabilities (perplexity, coherence).
Following principles from multimodal benchmarking frameworks (Wanaskar et al., 2025), our
evaluation emphasizes structured, deterministic metrics over subjective assessments.
**Q17. You train on 10 instruction types. Does CITA generalize to the 11th unseen type?**
Partially. Generalization depends on semantic similarity:


Paraphrase of trained type _∼_ 90% “formal” _≈_ “professional”
Combination of trained types _∼_ 75% “concise + professional”
Semantically similar new type _∼_ 60% “academic” _≈_ “educational”
Completely novel type _∼_ 30% “Socratic questioning”
**Limitation acknowledged** : CITA is not zero-shot for arbitrary instructions. Novel instruction types
require fine-tuning examples. This is consistent with how LLMs learn any new behavior.

Generalization Type Success Rate Example


SFT_Instruct [https://huggingface.co/anonymousML123/llama3-8b-pku-SFT-Instr](https://huggingface.co/anonymousML123/llama3-8b-pku-SFT-Instruct-Baseline-NoInstruct)
[uct-Baseline-NoInstruct](https://huggingface.co/anonymousML123/llama3-8b-pku-SFT-Instruct-Baseline-NoInstruct)
DPO_Instruct [https://huggingface.co/anonymousML123/llama3-8b-pku-DPO-Instr](https://huggingface.co/anonymousML123/llama3-8b-pku-DPO-Instruct-SFT-Instruct)
[uct-SFT-Instruct](https://huggingface.co/anonymousML123/llama3-8b-pku-DPO-Instruct-SFT-Instruct)
CITA_Instruct [https://huggingface.co/anonymousML123/llama3-8b-pku-CITA-Instr](https://huggingface.co/anonymousML123/llama3-8b-pku-CITA-Instruct-DPO-Instruct)
[uct-DPO-Instruct](https://huggingface.co/anonymousML123/llama3-8b-pku-CITA-Instruct-DPO-Instruct)
Hyperparameters Table 23 (exact values from Optuna)
Training Logs TensorBoard logs in repository
**Compute requirement** : A100-40GB for SFT/DPO/CITA, A100-80GB for PPO/GRPO (online
methods), _∼_ 4.5 hours total. Any academic lab can reproduce.
**Q19. Who would actually use CITA in production? What’s the practical deployment scenario?**
Three concrete deployment scenarios:
(a) **Multi-tenant AI platforms** : One CITA model serves different customers with different safety
policies (e.g., strict for healthcare, balanced for general use) via instruction switching—no
per-customer fine-tuning needed.
(b) **AI agent orchestration** : Agents dynamically adjust alignment based on task context:
Code review: "Be precise and critical"
User support: "Be empathetic and helpful"
(c) **Regulatory compliance** : Different jurisdictions require different content policies. CITA enables
runtime policy switching without model swaps.
**Cost savings** : Maintaining N policy-specific DPO models costs N _×_ compute/storage. One CITA
model with N instruction templates costs 1 _×_ .
**Q20. What is “mode collapse” and why does it matter for instruction-conditioned alignment?**
**Mode collapse** occurs when a model converges to a single dominant behavioral pattern, losing the
ability to express diverse outputs (Liu et al., 2024a).


Behavioral diversity Low (one dominant mode) High (multiple modes)
Instruction response Cannot switch Can switch dynamically
TruthfulQA ∆ +0.001 (DPO) +0.054 (CITA)
**Why it matters** : Instruction-conditioned alignment _requires_ multiple behavioral modes:
   - Mode 1: “Be concise” _→_ short responses
   - Mode 2: “Be detailed” _→_ comprehensive responses
   - Mode 3: “Be formal” _→_ professional tone
A mode-collapsed model produces similar outputs regardless of instruction—exactly what we observe
with DPO (+0.001 instruction sensitivity). CITA’s explicit KL prevents this collapse by maintaining
proximity to the reference policy’s behavioral diversity.
**Q21. Both DPO and CITA have KL regularization. Why does DPO’s implicit KL fail while CITA’s**


The critical difference is **parameter coupling** :


KL control _β_ (shared) _λ_ KL (separate)
Preference strength _β_ (shared) _β_ (separate)
Independent tuning ✗ No ✓ Yes
Mode behavior Mode-seeking Mode-preserving
**DPO’s problem** : In DPO’s derivation, _β_ controls _both_ the KL constraint strength _and_ preference
learning sharpness (Rafailov et al., 2023). You cannot increase KL regularization without also dampening preference learning. Research confirms: “commonly used settings such as low regularization
strength tend to specify _unimodal_ target distributions” (Liu et al., 2024a).
**CITA’s solution** : By separating _λ_ KL from _β_, CITA can:
(a) Set _β_ high for sharp preference learning
(b) Set _λ_ KL to maintain behavioral diversity
(c) Find the sweet spot that achieves _both_ goals
This decoupling is why CITA achieves 86.7% instruction-alignment efficiency while DPO achieves
only 56.1%—a 30.6 percentage point (pp) gap explained entirely by KL architecture.
**Q22. Recent research claims “reverse KL is designed to mode collapse.” Does this invalidate CITA?**
No—this research (Liu et al., 2024a; Goyal et al., 2024) actually _supports_ CITA’s design:
(a) **The critique applies to DPO** : The finding that “the mode-seeking property of reverse KL
divergence tends to reduce diversity” (Goyal et al., 2024) explains _why DPO fails_ at instructionswitching, not why CITA fails.
(b) **CITA’s explicit KL is the fix** : The same research proposes “explicit KL regularization acts as a
rehearsal mechanism” that “forces the model to maintain broad solution coverage” (Wang et al.,
2024c). This is exactly what CITA implements.
(c) **Empirical validation** : If reverse KL caused mode collapse in CITA, we’d see poor instruction
sensitivity. Instead, CITA shows 54 _×_ better TruthfulQA adaptation than DPO—evidence that
explicit KL _prevents_ the collapse that implicit KL permits.
**Key insight** : The problem isn’t reverse KL _per se_ —it’s _implicit, uncontrolled_ reverse KL. CITA’s
explicit, tunable KL avoids the trap.
**Q23. Could CITA be “cheating” by learning an instruction-specific routing trick (a shallow**
**prefix** _→_ **style map) rather than a genuinely switchable alignment policy? How do you rule out**