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RL-Forgetting-Experiments-3
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filtered-sft-20261019-artifacts

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rl-forgetting
filtered-sft
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The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 1 new columns ({'idx'}) and 10 missing columns ({'pass@4', 'pass@1', 'ground_truth', 'pass@32', 'pass@64', 'pass@2', 'pass@16', 'prompt', 'pass@128', 'pass@8'}).

This happened while the json dataset builder was generating data using

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'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_1072/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_1072/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_1072/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_214/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_214/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_214/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_322/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_322/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_322/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_429/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_429/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_429/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_536/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_536/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_536/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_643/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_643/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_643/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_750/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_750/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_750/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_858/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_858/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_858/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_965/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_965/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_ordered_lr1e-5_bs256_seed42_s4000_drop_saturated_rpuniform_lam1.0/eval/step_965/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_107/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_107/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_107/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_1072/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_1072/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_1072/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_214/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_214/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_214/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_322/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_322/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_322/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_429/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_429/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_429/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_536/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_536/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_536/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_643/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_643/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_643/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_750/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_750/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_750/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_858/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_858/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_858/summary.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_965/details.json', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_965/per_prompt.jsonl', 'hf://datasets/RL-Forgetting-Experiments-3/filtered-sft-20261019-artifacts@3dbeee14b3bf155a524e2afc8bc13c33d44d484c/runs/qwen3b_math_shuffled_lr1e-5_bs256_seed42_s4000_drop_saturated/eval/step_965/summary.json']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              idx: int64
              difficulty: string
              num_correct: int64
              n: int64
              samples: list<item: struct<text: string, correct: bool>>
                child 0, item: struct<text: string, correct: bool>
                    child 0, text: string
                    child 1, correct: bool
              to
              {'prompt': Value('string'), 'ground_truth': Value('string'), 'difficulty': Value('string'), 'n': Value('int64'), 'num_correct': Value('int64'), 'samples': List({'text': Value('string'), 'correct': Value('bool')}), 'pass@1': Value('float64'), 'pass@2': Value('float64'), 'pass@4': Value('float64'), 'pass@8': Value('float64'), 'pass@16': Value('float64'), 'pass@32': Value('float64'), 'pass@64': Value('float64'), 'pass@128': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              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 1702, 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 1858, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 1 new columns ({'idx'}) and 10 missing columns ({'pass@4', 'pass@1', 'ground_truth', 'pass@32', 'pass@64', 'pass@2', 'pass@16', 'prompt', 'pass@128', 'pass@8'}).
              
              This happened while the json dataset builder was generating data using
              
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              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
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prompt string	ground_truth string	difficulty string	n int64	num_correct int64	samples list	pass@1 float64	pass@2 float64	pass@4 float64	pass@8 float64	pass@16 float64	pass@32 float64	pass@64 float64	pass@128 float64
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	2.5	0/8	160	1	[{"text":"## Step 1: Understand the problem\nWe need to find the smallest possible median of the six(...TRUNCATED)	0.00625	0.0125	0.025	0.05	0.1	0.2	0.4	0.8
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	20	7/8	160	0	[{"text":"## Step 1: Understand the problem\nWe are given two teams, Team A and Team B, each with n (...TRUNCATED)	0	0	0	0	0	0	0	0
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	2ef\cosP=-^2+b^2-^2+^2	4/8	160	0	[{"text":"## Step 1: Understand the problem statement\nWe are given a quadrilateral \$ABCD\$ with (...TRUNCATED)	0	0	0	0	0	0	0	0
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	12	4/8	160	2	[{"text":"## Step 1: Analyze the given figure and identify key geometric properties\nWe are given t(...TRUNCATED)	0.0125	0.024921	0.049528	0.097799	0.190566	0.361006	0.641509	0.961006
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	\frac{\sqrt{5}}{5}	7/8	160	0	[{"text":"## Step 1: Recall the definition of the circumcenter of a triangle\nThe circumcenter of a (...TRUNCATED)	0	0	0	0	0	0	0	0
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	(1, p)	0/8	160	5	[{"text":"## Step 1: Understand the problem and the constraints\nWe are given the equation $x^{p-1}$(...TRUNCATED)	0.03125	0.061714	0.120343	0.22881	0.413679	0.677509	0.925493	0.999755
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	4	2/8	160	0	[{"text":"## Step 1: Identify the given information\nWe are given an isosceles triangle \$ ABC \$ (...TRUNCATED)	0	0	0	0	0	0	0	0
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	30	3/8	160	0	[{"text":"## Step 1: Understand the problem\nThe problem involves an n x n chessboard where six pawn(...TRUNCATED)	0	0	0	0	0	0	0	0
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	12	2/8	160	0	[{"text":"## Step 1: Understand the problem\nWe are given a circle with center \$ O \$ and radius (...TRUNCATED)	0	0	0	0	0	0	0	0
"<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>\n\nCutting Knowledge Date: December 202(...TRUNCATED)	\frac{57}{5}	2/8	160	0	[{"text":"## Step 1: Recall the definition of the cotangent function\nThe cotangent of an angle \\(\(...TRUNCATED)	0	0	0	0	0	0	0	0
End of preview.
Filtered SFT delivery, October 19 2026

Incremental delivery from the 15-arm filtered SFT experiment. Incomplete runs must not be treated as complete studies. Check the dataset delivery_manifest.json for verified upload units and hashes. Model checkpoints are under runs/checkpoints/step_N/model; final models under runs/final. Load with the corresponding subfolder.
Built with Qwen: Qwen2.5-3B derivatives retain the upstream Qwen RESEARCH LICENSE AGREEMENT. Built with Llama: Llama derivatives use Llama 3.2 and subject to the Llama 3.2 Community License and Acceptable Use Policy; see https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct. No additional rights to base models are granted.
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