Towards Practical World Model-based Reinforcement Learning for Vision-Language-Action Models
Paper • 2603.20607 • Published
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Error code: InfoError
Exception: HfHubHTTPError
Message: (Request ID: Root=1-6a672f4d-170f7e8f647488014a13a6d5;d18e573c-85b8-4bf9-9e94-933a88d0796b)
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Url: https://huggingface.co/api/datasets/srilearns/vla-mbpo-repro/revision/e385ee4a4fb76ed3758d7bde906905c5c7047dc3.
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Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 227, in compute_first_rows_from_streaming_response
info = get_dataset_config_info(path=dataset, config_name=config, token=hf_token)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 268, in get_dataset_config_info
builder = load_dataset_builder(
path,
...<6 lines>...
**config_kwargs,
)
File "/src/services/worker/src/worker/utils.py", line 390, in safe_load_dataset_builder
dataset_module = dataset_module_factory(
repo_dir,
revision=revision,
download_config=download_config,
)
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1217, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1192, in dataset_module_factory
).get_module()
~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 608, in get_module
standalone_yaml_path = cached_path(
hf_dataset_url(self.name, config.REPOYAML_FILENAME, revision=self.commit_hash),
download_config=download_config,
)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 180, in cached_path
).resolve_path(url_or_filename)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 339, in resolve_path
repo_and_revision_exist, err = self._repo_and_revision_exist(parsed.type, parsed.id, revision)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_file_system.py", line 252, in _repo_and_revision_exist
self._api.repo_info(
~~~~~~~~~~~~~~~~~~~^
repo_id, revision=revision, repo_type=repo_type, timeout=constants.HF_HUB_ETAG_TIMEOUT
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 88, in _inner_fn
return fn(*args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_api.py", line 3598, in repo_info
return method(
repo_id,
...<4 lines>...
files_metadata=files_metadata,
)
File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py", line 88, in _inner_fn
return fn(*args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/huggingface_hub/hf_api.py", line 3360, in dataset_info
hf_raise_for_status(r)
~~~~~~~~~~~~~~~~~~~^^^
File "/usr/local/lib/python3.14/site-packages/huggingface_hub/utils/_http.py", line 868, in hf_raise_for_status
raise _format(HfHubHTTPError, message, response) from e
huggingface_hub.errors.HfHubHTTPError: (Request ID: Root=1-6a672f4d-170f7e8f647488014a13a6d5;d18e573c-85b8-4bf9-9e94-933a88d0796b)
429 Too Many Requests: you have reached your 'api' rate limit.
Retry after 119 seconds (0/500 requests remaining in current 300s window).
Url: https://huggingface.co/api/datasets/srilearns/vla-mbpo-repro/revision/e385ee4a4fb76ed3758d7bde906905c5c7047dc3.
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VLA-MBPO (ICML 2026) — independent reproduction bundle
Reproduction of "Towards Practical World Model-based Reinforcement Learning for
Vision-Language-Action Models" (VLA-MBPO), ICML 2026, OpenReview yKQ8GrwEhr,
arXiv:2603.20607.
No official code/checkpoints were released; this is an independent reproduction from the paper text. Trackio logbook is the primary record.
Contents
| Path | What it is |
|---|---|
paper/paper.txt |
Extracted full text of the arXiv PDF (27 pages) |
paper/vla_mbpo_arxiv.pdf |
The paper PDF |
claim3_value_gap.py |
Claim 3 (theory): recomputes the value-gap constants, verifies Thm 4.2 from Lemma A.4 with sympy, and validates both bounds on 200 tabular MDPs |
claim3_figure.py |
Plotly figure for Claim 3 |
claims45_audit.py |
Claims 4 & 5: internal-consistency audit of Tables 2/3/5 |
claim2_world_model.py |
Claim 2/1: reduced-scale multi-view world model (IVD vs parallel, pretrained vs random, joint vs separate reward) on LIBERO-Object |
claim2_world_model_job.py |
Same script with a PEP 723 header, for hf jobs uv run |
outputs/claim3/ |
Claim 3 results JSON + figure |
outputs/claims45/ |
Claims 4 & 5 audit JSON |
outputs/claim2/ |
Claim 2 results (populated by the GPU job) |
Reproduce
# Claim 3 — theory (seconds, CPU)
python claim3_value_gap.py --seeds 200
python claim3_figure.py
# Claims 4 & 5 — table audit (instant, CPU)
python claims45_audit.py
# Claim 2 — world model (GPU; ~45 min, ~$0.60 on one L4)
hf jobs uv run --flavor l4x1 --timeout 2h --secrets HF_TOKEN \
-e HF_HUB_ENABLE_HF_TRANSFER=1 -d claim2_world_model_job.py -- \
--steps 8000 --batch-size 128 --dim 384 --layers 6 \
--reward-weight 0.5 --workers 8 --push-to srilearns/vla-mbpo-repro
Data: physical-intelligence/libero (LIBERO-Object suite, tasks 20–29).
Base UMM used at full scale by the paper (not by this reduced-scale run):
ByteDance-Seed/BAGEL-7B-MoT.
Findings (summary)
- Claim 3 — reproduced. All four case-study constants match exactly (4183.32, 18916.58, 1710.78, 400.00); Thm 4.2 is a correct consequence of Lemma A.4; both bounds hold on 200/200 random MDPs. Caveat: the headline "47×" model-error tightening is a step-vs-chunk unit mismatch — ~4.7× in matched units.
- Claim 5 — refuted as literally stated. Table 5 shows 2 of 12 hyperparameters vary (Sample size 512→1280, Update-to-data 20→50 on LIBERO-Long); the paper's own appendix admits this. True in spirit (10/12 constant across 5 suites and 2 platforms).
- Claim 4 — not reproduced (infeasible); numbers internally consistent. One typo found (Goal 92.8 should be 92.6); it does not affect any headline claim.
- Claims 1 & 2 — mechanism code validated end-to-end; GPU run pending.
- Claim 6 — not verifiable (no robots, no data, no numbers, no video).
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