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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1401, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1579, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
pt unknown | __key__ string | __url__ string |
|---|---|---|
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAYTI1ZmEwYmFlYjRjZTk4ODk4NTcvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/a25fa0baeb4ce9889857 | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAMGY5ZjdmZGJiOTk5YTdiZDA0MzMvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/0f9f7fdbb999a7bd0433 | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAZDg5MTZlOTU1OThiM2QyZWJlNTcvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/d8916e95598b3d2ebe57 | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAN2VlMjIzYWI4YWFiZTM0YmZhMTUvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/7ee223ab8aabe34bfa15 | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAOTFkMmJmYzc4MzY5MThmMzFkNDgvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/91d2bfc7836918f31d48 | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAZmExZWQxNjM5YWJkN2JkYjI2M2EvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/fa1ed1639abd7bdb263a | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAYjgyNTc2NWFlNzNmZjUyZDQ2NmQvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/b825765ae73ff52d466d | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAMTA4ZWZkZGY0NzY5NjgyNjI5MjMvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/108efddf476968262923 | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAMTNhMzBiMjZhMmM4MTE4NWRkMmMvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/13a30b26a2c81185dd2c | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
"UEsDBAAACAgAAAAAAAAAAAAAAAAAAAAAAAAdAAUAOTBkYzQ1YmVlNzE1ZjlmMmJlZmUvZGF0YS5wa2xGQgEAWoACfXEAKFgDAAA(...TRUNCATED) | transitions/90dc45bee715f9f2befe | "hf://datasets/hminjeong/mcts_rl_label@1c35dc4d6553993bc7173e180277c235437eaaa4/archives/chartqa-000(...TRUNCATED) |
End of preview.
MCTS Routing Transitions
PUCT iteration-1 experience for visual layer routing in Qwen2.5-VL-7B:
512 inputs, 777,402 states, and 843,666 unique transitions.
Each .pt file contains one input's observed routing graph.
Contents
uid Input identifier
features Router observations indexed by state
prefixes Exact action history identifying each state
transitions:
state Current state index
action 0 = FREEZE, 1 = ON
next_state Successor state index; -1 at termination
terminal Whether this transition ends the route
reward Native answer score at termination; 0 otherwise
layer Decoder layer index (0–27)
cost_flops This layer's decoder-prefill FLOPs
cost_fraction cost_flops / Dense decoder-prefill FLOPs
utility_reward reward - 0.05 * cost_fraction
routes Complete masks, scores, occurrence counts, and origins
cost Dense, ON-layer, and FREEZE-layer FLOPs
source Historical provenance paths and source hashes
protocol_sha256 Collection/reconstruction protocol fingerprint
FREEZE preserves visual hidden states and access to visual K/V; ON updates them. Rewards retain native partial scores. Repeated edges are deduplicated; failed routes are retained. Terminal transitions must not bootstrap from a next state.
Quick start
Install huggingface_hub and PyTorch, then run:
hf download hminjeong/mcts_rl_label download.py --repo-type dataset --local-dir .
python download.py --output ./mcts_rl_label
The script downloads, verifies, and extracts the independent archive shards.
Use --benchmark docvqa to download only one benchmark.
from pathlib import Path
import torch
path = next(Path("mcts_rl_label/transitions").glob("*.pt"))
data = torch.load(path, map_location="cpu", weights_only=True, mmap=True)
tr = data["transitions"]
observations = {k: v[tr["state"][:32]] for k, v in data["features"].items()}
manifest.jsonl maps UIDs to files and shards, with counts and SHA-256 hashes.
Original .pt bytes are preserved. Historical paths in source are provenance
only; loading does not require them. Backbone weights and raw images are not included.
The repository's Apache-2.0 designation does not replace upstream dataset/model terms.
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