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Datasets:
JG1310
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repro-chain-of-thought-gradient-descent-code-sol

Tags:
icml2026-repro
chain-of-thought
in-context-learning
reproducibility
License:
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The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
direction: struct<concept: string, canvas: string, layout: string, density: string, palette_source: string, acc (... 129 chars omitted)
  child 0, concept: string
  child 1, canvas: string
  child 2, layout: string
  child 3, density: string
  child 4, palette_source: string
  child 5, accent: string
  child 6, accent_deep: string
  child 7, emphasis: string
  child 8, typography: string
  child 9, surface: string
  child 10, section_joint: string
  child 11, identity: string
dark_ground: bool
colors: struct<accent: string, accent_deep: string, accent_light: string, accent_soft: string, accent_ink: s (... 57 chars omitted)
  child 0, accent: string
  child 1, accent_deep: string
  child 2, accent_light: string
  child 3, accent_soft: string
  child 4, accent_ink: string
  child 5, emph: string
  child 6, emph_soft: string
  child 7, emph_ink: string
seed: int64
learning_rate: double
eval_depths: list<item: int64>
  child 0, item: int64
d: int64
model_dim: int64
transformer_layers: int64
selected_tokens_per_update: int64
optimizer_lr: double
batch_size: int64
heads: int64
rollout_rounds: int64
train_depths: list<item: int64>
  child 0, item: int64
steps: int64
to
{'seed': Value('int64'), 'd': Value('int64'), 'model_dim': Value('int64'), 'heads': Value('int64'), 'steps': Value('int64'), 'batch_size': Value('int64'), 'learning_rate': Value('float64'), 'optimizer_lr': Value('float64'), 'train_depths': List(Value('int64')), 'eval_depths': List(Value('int64')), 'rollout_rounds': Value('int64'), 'transformer_layers': Value('int64'), 'selected_tokens_per_update': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 489, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              direction: struct<concept: string, canvas: string, layout: string, density: string, palette_source: string, acc (... 129 chars omitted)
                child 0, concept: string
                child 1, canvas: string
                child 2, layout: string
                child 3, density: string
                child 4, palette_source: string
                child 5, accent: string
                child 6, accent_deep: string
                child 7, emphasis: string
                child 8, typography: string
                child 9, surface: string
                child 10, section_joint: string
                child 11, identity: string
              dark_ground: bool
              colors: struct<accent: string, accent_deep: string, accent_light: string, accent_soft: string, accent_ink: s (... 57 chars omitted)
                child 0, accent: string
                child 1, accent_deep: string
                child 2, accent_light: string
                child 3, accent_soft: string
                child 4, accent_ink: string
                child 5, emph: string
                child 6, emph_soft: string
                child 7, emph_ink: string
              seed: int64
              learning_rate: double
              eval_depths: list<item: int64>
                child 0, item: int64
              d: int64
              model_dim: int64
              transformer_layers: int64
              selected_tokens_per_update: int64
              optimizer_lr: double
              batch_size: int64
              heads: int64
              rollout_rounds: int64
              train_depths: list<item: int64>
                child 0, item: int64
              steps: int64
              to
              {'seed': Value('int64'), 'd': Value('int64'), 'model_dim': Value('int64'), 'heads': Value('int64'), 'steps': Value('int64'), 'batch_size': Value('int64'), 'learning_rate': Value('float64'), 'optimizer_lr': Value('float64'), 'train_depths': List(Value('int64')), 'eval_depths': List(Value('int64')), 'rollout_rounds': Value('int64'), 'transformer_layers': Value('int64'), 'selected_tokens_per_update': Value('int64')}
              because column names don't match

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Chain-of-Thought Gradient Descent — independent reproduction

This directory contains a scaled, self-contained reproduction of the mechanisms behind ICML 2026 paper #443, Chain-of-Thought Gradient Descent.

The paper does not provide code or complete training hyperparameters. This reproduction therefore tests its two central mechanisms directly:

  1. A frozen, one-layer decoder-style attention block is trained once and reused to emit local ReLU-network forward and backward blocks. It receives exactly two routed state tokens per update and is evaluated by teacher forcing, autoregressive rollout, transfer to deeper networks, and recursive reuse across gradient-descent rounds.
  2. A dynamic-mask benchmark compares processing two relevant weight matrices with processing all N matrices packed into every update. It reports both exact scalar-operation counts and measured device latency.

The experiment is intentionally scaled (d=4, small synthetic networks) and must be interpreted as a mechanism-level reproduction, not a full replication of the paper's unavailable training setup.

Run 

python scripts/run_reproduction.py --smoke --output-dir outputs/smoke
python scripts/run_reproduction.py --steps 3000 --batch-size 256 \
  --output-dir outputs/gpu
python scripts/make_figures.py --results outputs/gpu/results.json \
  --output-dir figures

All randomness is seeded. The main script records its configuration, package versions, device, wall-clock time, per-depth errors, multi-round rollout errors, dynamic-mask routing checks, exact cost counts, and timing measurements in one JSON file.

Reproduced result

The substantive run completed on one NVIDIA L4 in 912.1 seconds:

Metric N=3 N=6 (unseen) N=9 (unseen)
Teacher-forced local component MSE 0.005464 0.003753 0.003467
Round-1 free-running weight RMSE 0.02251 0.009306 0.008379
Round-10 free-running weight RMSE 0.07433 0.06392 0.06631

The exact packed/masked processed-matrix ratio is N/2, hence Theta(N). Measured packed/masked L4 latency reached 4.42x at N=64 and 7.35x at N=128; this is lower than the exact operation-count ratio because fixed kernel overhead dominates at d=4.

Claim 1 and Claim 2 are therefore supported at toy scale/mechanism level. This does not establish the paper's arbitrary-depth theorem empirically, recreate its unavailable explicit attention weights, or reproduce its larger multi-seed Appendix E setup.
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