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reasoning-cues
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rollouts-olmo7b-rlzero

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
pass@1: double
pass@1_ci95: list<item: double>
  child 0, item: double
pass@1_sd_boot: double
pass@1_by_rollout_index: list<item: double>
  child 0, item: double
pass@1_sd_rollouts: double
pass@8: double
pass@8_ci95: list<item: double>
  child 0, item: double
pass@8_sd_boot: double
pass@16: double
pass@16_ci95: list<item: double>
  child 0, item: double
pass@16_sd_boot: double
cap_rate: double
n_problems: int64
n_rollouts: int64
rollouts_per_problem: int64
max_tokens_used: list<item: int64>
  child 0, item: int64
prompt_style: list<item: null>
  child 0, item: null
median_output_tokens: int64
mean_output_tokens: double
expected_problems: int64
complete: bool
files: list<item: string>
  child 0, item: string
per_problem: struct<aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list (... 8176 chars omitted)
  child 0, aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list<item: int64>, answ (... 161 chars omitted)
      child 0, rollout_index: list<item: int64>
          child 0, item: int64
      child 1, correct: list<item: int64>
          child 0, item: int64
      child 2, tokens: list<item: int64>
          child 0, item: int64
      child 3, answer: list<item: string>
          child 0, item: string
      child 4, hit_cap: list<item: int64>
          child 0, item: int64
      child 5, answer_end_frac: list<item: double>
          child 0, item: double
      child 6, answer_marker: list<item: s
...
 child 0, k: int64
      child 1, expected_tokens: double
      child 2, pass_at_k: double
      child 3, majority_at_k: double
  child 27, 28: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
      child 0, k: int64
      child 1, expected_tokens: double
      child 2, pass_at_k: double
      child 3, majority_at_k: double
  child 28, 29: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
      child 0, k: int64
      child 1, expected_tokens: double
      child 2, pass_at_k: double
      child 3, majority_at_k: double
  child 29, 30: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
      child 0, k: int64
      child 1, expected_tokens: double
      child 2, pass_at_k: double
      child 3, majority_at_k: double
  child 30, 31: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
      child 0, k: int64
      child 1, expected_tokens: double
      child 2, pass_at_k: double
      child 3, majority_at_k: double
  child 31, 32: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
      child 0, k: int64
      child 1, expected_tokens: double
      child 2, pass_at_k: double
      child 3, majority_at_k: double
aggregate_by_k_doc: string
correct: bool
output_tokens: int64
committed: bool
problem_key: string
hit_token_cap: bool
steer: string
completion: string
label: string
rollout_index: int64
finish_reason: string
to
{'problem_key': Value('string'), 'rollout_index': Value('int64'), 'arm': Value('string'), 'steer': Value('string'), 'label': Value('string'), 'committed': Value('bool'), 'correct': Value('bool'), 'hit_token_cap': Value('bool'), 'finish_reason': Value('string'), 'output_tokens': Value('int64'), 'max_tokens_used': Value('int64'), 'completion': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1827, in _prepare_split_single
                  for key, table 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/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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              pass@1: double
              pass@1_ci95: list<item: double>
                child 0, item: double
              pass@1_sd_boot: double
              pass@1_by_rollout_index: list<item: double>
                child 0, item: double
              pass@1_sd_rollouts: double
              pass@8: double
              pass@8_ci95: list<item: double>
                child 0, item: double
              pass@8_sd_boot: double
              pass@16: double
              pass@16_ci95: list<item: double>
                child 0, item: double
              pass@16_sd_boot: double
              cap_rate: double
              n_problems: int64
              n_rollouts: int64
              rollouts_per_problem: int64
              max_tokens_used: list<item: int64>
                child 0, item: int64
              prompt_style: list<item: null>
                child 0, item: null
              median_output_tokens: int64
              mean_output_tokens: double
              expected_problems: int64
              complete: bool
              files: list<item: string>
                child 0, item: string
              per_problem: struct<aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list (... 8176 chars omitted)
                child 0, aime2024_0: struct<rollout_index: list<item: int64>, correct: list<item: int64>, tokens: list<item: int64>, answ (... 161 chars omitted)
                    child 0, rollout_index: list<item: int64>
                        child 0, item: int64
                    child 1, correct: list<item: int64>
                        child 0, item: int64
                    child 2, tokens: list<item: int64>
                        child 0, item: int64
                    child 3, answer: list<item: string>
                        child 0, item: string
                    child 4, hit_cap: list<item: int64>
                        child 0, item: int64
                    child 5, answer_end_frac: list<item: double>
                        child 0, item: double
                    child 6, answer_marker: list<item: s
              ...
               child 0, k: int64
                    child 1, expected_tokens: double
                    child 2, pass_at_k: double
                    child 3, majority_at_k: double
                child 27, 28: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
                    child 0, k: int64
                    child 1, expected_tokens: double
                    child 2, pass_at_k: double
                    child 3, majority_at_k: double
                child 28, 29: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
                    child 0, k: int64
                    child 1, expected_tokens: double
                    child 2, pass_at_k: double
                    child 3, majority_at_k: double
                child 29, 30: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
                    child 0, k: int64
                    child 1, expected_tokens: double
                    child 2, pass_at_k: double
                    child 3, majority_at_k: double
                child 30, 31: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
                    child 0, k: int64
                    child 1, expected_tokens: double
                    child 2, pass_at_k: double
                    child 3, majority_at_k: double
                child 31, 32: struct<k: int64, expected_tokens: double, pass_at_k: double, majority_at_k: double>
                    child 0, k: int64
                    child 1, expected_tokens: double
                    child 2, pass_at_k: double
                    child 3, majority_at_k: double
              aggregate_by_k_doc: string
              correct: bool
              output_tokens: int64
              committed: bool
              problem_key: string
              hit_token_cap: bool
              steer: string
              completion: string
              label: string
              rollout_index: int64
              finish_reason: string
              to
              {'problem_key': Value('string'), 'rollout_index': Value('int64'), 'arm': Value('string'), 'steer': Value('string'), 'label': Value('string'), 'committed': Value('bool'), 'correct': Value('bool'), 'hit_token_cap': Value('bool'), 'finish_reason': Value('string'), 'output_tokens': Value('int64'), 'max_tokens_used': Value('int64'), 'completion': Value('string')}
              because column names don't match
              
              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 1694, 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 1880, 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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problem_key string	rollout_index int64	arm string	steer string	label string	committed bool	correct bool	hit_token_cap bool	finish_reason string	output_tokens int64	max_tokens_used int64	completion string
aime2024_0	0	none	base	run	true	true	false	stop	2,715	31,744	Okay, so there's this problem about Aya going for a walk and then stopping at a coffee shop. The problem gives two different speeds she walks at and the corresponding times it takes, including the time she spends in the coffee shop. Then they ask what the time would be if she walks at a different speed. Hmm, interes...
aime2024_0	1	none	base	run	true	true	false	stop	3,724	31,744	Okay, so there's this problem about Aya walking to a coffee shop. Let me read it again to make sure I get all the details. "Aya goes for a 9-kilometer-long walk every morning and stops at a coffee shop afterwards. When she walks at a constant speed of s kilometers per hour, the walk takes her 4 hours, including t m...
aime2024_0	2	none	base	run	true	false	true	length	31,744	31,744	We are told that when Aya walks at a speed of $s$ km/h, the total time for the walk and the coffee shop stop is 4 hours. When she walks at $s+2$ km/h, the total time is 2 hours and 24 minutes (which is $2 + \frac{24}{60} = 2.4$ hours). The coffee shop stop time $t$ is the same in both cases. 1. First scenario: ...
aime2024_0	3	none	base	run	false	false	true	length	31,744	31,744	" Okay, so there's this problem about Aya's walk to the coffee shop. Let me read it again to make su(...TRUNCATED)
aime2024_0	4	none	base	run	true	true	false	stop	3,632	31,744	" \n\nOkay, so there's this problem about Aya walking and stopping at a coffee shop. The problem sta(...TRUNCATED)
aime2024_0	5	none	base	run	true	false	false	stop	420	31,744	" \nFirst, let's find the value of $s$ and $t$. \nFrom the first scenario: \n$$\n9 = s \\cdot 4 +(...TRUNCATED)
aime2024_0	6	none	base	run	false	false	true	length	31,744	31,744	" Let $d$ be the number of minutes Aya spends walking. We are given that the total time for the walk(...TRUNCATED)
aime2024_0	7	none	base	run	false	false	true	length	31,744	31,744	" \n\nOkay, so there's this problem about Aya going for a walk and then stopping at a coffee shop. T(...TRUNCATED)
aime2024_0	8	none	base	run	true	true	false	stop	2,819	31,744	" \n\nOkay, so Aya goes for a 9-kilometer walk every morning and then stops at a coffee shop. The pr(...TRUNCATED)
aime2024_0	9	none	base	run	true	false	false	stop	250	31,744	" \nFirst, we need to find the value of $s$. We can set up two equations based on the given informat(...TRUNCATED)

End of preview.

rollouts-olmo7b-rlzero — evaluation rollouts

Model: allenai/Olmo-3-7B-RL-Zero-Math. Tokenizer used for answer positions: allenai/Olmo-3-7B-RL-Zero-Math. Protocol: 32 rollouts per problem (two seeded batches of 16), temperature 0.6, top-p 0.95, budget 31,744 generated tokens, seed 20260819. Prompts and grader: the paper's repository (sophicle/reason). Rollout jsonl files are kept as written (one graded rollout per line, with the completion), under the run directories as on disk (run/, run_regraded/, *_execgraded/); cell summaries are at <prompt>/<benchmark>/summary/<arm>.json.

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