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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 1531, in _prepare_split_single
                  for key, record in generator:
                                     ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
                  for item in generator(*args, **kwargs):
                              ~~~~~~~~~^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 127, 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 1405, 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 982, 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 786, 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 1369, 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 948, 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 1393, 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 1571, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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jsonl
unknown
__key__
string
__url__
string
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12rambau__sepal_ui-574_group_info
hf://datasets/sweagent/combo2-rl-rollouts@eaff5487b8983db9aaed9e93c16e670695389827/group_info.tar.gz
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15five__scim2-filter-parser-13_group_info
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20c__ctl-3_group_info
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aai-institute__nnbench-112_group_info
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aai-institute__nnbench-171_group_info
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ably__ably-python-421_group_info
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ably__ably-python-457_group_info
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acorg__dark-matter-610_group_info
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adamchainz__apig-wsgi-118_group_info
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adamchainz__apig-wsgi-80_group_info
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adamchainz_django-cors-headers_pr397_group_info
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adamchainz__flake8-comprehensions-143_group_info
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adamchainz__flake8-comprehensions-205_group_info
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adamtheturtle_doccmd_pr383_group_info
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adamtheturtle_doccmd_pr42_group_info
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adbar__simplemma-114_group_info
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aeye-lab__pymovements-1108_group_info
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aeye-lab__pymovements-408_group_info
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agronholm__anyio-604_group_info
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agronholm__anyio-707_group_info
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agronholm__anyio-735_group_info
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ahawker__ulid-59_group_info
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AI4S2S__lilio-58_group_info
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aiogram__aiogram-1665_group_info
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aio-libs__aiohttp-7713_group_info
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aio-libs__aiohttp-7865_group_info
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aio-libs__aiohttp-8098_group_info
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aio-libs__aiohttp-9017_group_info
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aio-libs__aiosmtpd-143_group_info
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aio-libs__aiosmtpd-242_group_info
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aio-libs__yarl-1414_group_info
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akaihola__darker-349_group_info
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alanmcruickshank__sqlfluff-188_group_info
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alchemistry__alchemlyb-151_group_info
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alecthomas__voluptuous-380_group_info
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alexgolec__tda-api-37_group_info
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Algebra8__pyopenapi3-91_group_info
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algorand__py-algorand-sdk-171_group_info
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algorand__pyteal-29_group_info
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ali-ramadhan__matplotloom-7_group_info
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alteryx__woodwork-651_group_info
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alteryx__woodwork-682_group_info
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amaranth-lang__amaranth-1098_group_info
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amaranth-lang__amaranth-1317_group_info
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amaranth-lang__amaranth-1376_group_info
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amaranth-lang__amaranth-1395_group_info
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amaranth-lang__amaranth-923_group_info
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ambv__black-724_group_info
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amueller__word_cloud-242_group_info
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anchore__anchore-cli-101_group_info
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anchore__anchore-cli-107_group_info
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andersbogsnes__ml_tooling-752_group_info
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andialbrecht__sqlparse-445_group_info
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andrewgodwin__urlman-12_group_info
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ansible__ansible-builder-359_group_info
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ansible__ansible-runner-1334_group_info
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apiflask__apiflask-407_group_info
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apiflask__apiflask-413_group_info
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appium_python-client_pr351_group_info
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aristanetworks__anta-1021_group_info
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ArkEcosystem__python-crypto-116_group_info
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arm-doe__act-743_group_info
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ARMmbed__mbed-tools-138_group_info
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ARMmbed__yotta-804_group_info
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arviz-devs_preliz_pr158_group_info
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arviz-devs_preliz_pr354_group_info
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arviz-devs_preliz_pr356_group_info
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arviz-devs_preliz_pr394_group_info
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arviz-devs_preliz_pr395_group_info
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arviz-devs_preliz_pr651_group_info
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arviz-devs_preliz_pr94_group_info
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asdf-format__asdf-1825_group_info
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asottile__add-trailing-comma-178_group_info
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asottile__add-trailing-comma-18_group_info
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asottile__add-trailing-comma-20_group_info
hf://datasets/sweagent/combo2-rl-rollouts@eaff5487b8983db9aaed9e93c16e670695389827/group_info.tar.gz
[ 123, 34, 115, 97, 118, 101, 100, 95, 97, 116, 34, 58, 32, 34, 50, 48, 50, 54, 45, 48, 56, 45, 48, 54, 84, 48, 53, 58, 50, 49, 58, 49, 52, 46, 51, 52, 51, 54, 57, 54, 34, 44, 32, 34, 101, 120, 105, 116, 95, 115, 116, ...
asottile__add-trailing-comma-4_group_info
hf://datasets/sweagent/combo2-rl-rollouts@eaff5487b8983db9aaed9e93c16e670695389827/group_info.tar.gz
[ 123, 34, 115, 97, 118, 101, 100, 95, 97, 116, 34, 58, 32, 34, 50, 48, 50, 54, 45, 48, 56, 45, 48, 55, 84, 48, 52, 58, 49, 56, 58, 52, 49, 46, 48, 51, 52, 48, 49, 50, 34, 44, 32, 34, 101, 120, 105, 116, 95, 115, 116, ...
asottile__babi-328_group_info
hf://datasets/sweagent/combo2-rl-rollouts@eaff5487b8983db9aaed9e93c16e670695389827/group_info.tar.gz
End of preview.

combo2 RL rollouts — Qwen3.5-35B-A3B

Complete rollout + reward record for the combo2 GRPO run: every trajectory the policy generated during training, with its graded reward. Preserved so the run stays re-analysable after its torch_dist checkpoints were retired.

Run

base model Qwen3.5-35B-A3B
init sweagent/practical-diffrecon-ep2 (the iter-1 RFT ckpt)
harness combo (contract-ground + git-add-N + wall-clock valve)
algorithm GRPO with dynamic sampling, kl-loss-coef 0.00, lr 1e-6 constant
batch rollout-batch 32 x n-samples 4, GBS 128
steps 0-54
task pool combined_0630 (SWE-rebench V1/V2 + Scale-SWE)
grading azure-modal sandbox, F2P/P2P; resolved = full pass

Superseded by the base -> iter_49 -> iter-2 line; published as a record, not as the current method.

Contents

file items what
trajectories_00..05.tar.gz 27,787 full trajectories: messages, model_patch, exit_status, n_steps, prompt/response token_ids, loss_mask
rewards.tar.gz 26,610 per-sample grading: resolved, resolution, tests_run/passed, f2p/p2p rates, error
group_info.tar.gz 2,038 per-instance group records (saved_at, reward, sample_index, token_length)

34.8 GiB raw, ~2.8 GB gzipped. Files are named <instance_id>_<sample_index>.json (trajectory) and <instance_id>_<sample_index>_rewards.json (reward), so the two join on name.

Notes for re-analysis

  • Not every trajectory has a reward. Rollouts with no patch (exit_status of LimitsExceeded / TimeExceeded) are never graded — they count as unresolved. The ~1,200 gap between traj and reward counts is that, not data loss.
  • Per-step binning: these files carry no step field. Bin by file mtime against the training log's rollout N: markers, assigning a sample to the first marker at/after its mtime — that line prints when a finished batch is handed to actor_train, so a step's rollouts precede its marker.
  • resolved is the ground truth, not the in-pipeline resolve=N/N stdout print, which is known to read 0.
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