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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
domain: string
seed: int64
outer_fold: int64
train_acc_i1: double
train_acc_i2: double
train_acc_i3: double
train_prior: string
prior_tie: bool
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1221
to
{'method': Value('string'), 'question_id': Value('string'), 'split_group_id': Value('string'), 'candidate_group_id': Value('string'), 'benchmark': Value('string'), 'domain': Value('string'), 'seed': Value('int64'), 'outer_fold': Value('int64'), 'correct_i1': Value('int64'), 'correct_i2': Value('int64'), 'correct_i3': Value('int64'), 'raw_score_i1': Value('float64'), 'raw_score_i2': Value('float64'), 'raw_score_i3': Value('float64'), 'z_score_i1': Value('float64'), 'z_score_i2': Value('float64'), 'z_score_i3': Value('float64'), 'top1_iter': Value('float64'), 'top1_score': Value('float64'), 'top2_score': Value('float64'), 'top1_top2_gap': Value('float64'), 'unique_top1': Value('bool'), 'margin': Value('float64'), 'train_prior': Value('string'), 'fallback_triggered': Value('bool'), 'selected_iter': Value('string'), 'selected_correct': Value('int64'), 'representation_selected_iter': Value('string'), 'representation_selected_correct': Value('int64'), 'agreement_pattern': 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 1848, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 764, in write_table
                  self.write_rows_on_file()  # in case there are buffered rows to write first
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._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
              domain: string
              seed: int64
              outer_fold: int64
              train_acc_i1: double
              train_acc_i2: double
              train_acc_i3: double
              train_prior: string
              prior_tie: bool
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1221
              to
              {'method': Value('string'), 'question_id': Value('string'), 'split_group_id': Value('string'), 'candidate_group_id': Value('string'), 'benchmark': Value('string'), 'domain': Value('string'), 'seed': Value('int64'), 'outer_fold': Value('int64'), 'correct_i1': Value('int64'), 'correct_i2': Value('int64'), 'correct_i3': Value('int64'), 'raw_score_i1': Value('float64'), 'raw_score_i2': Value('float64'), 'raw_score_i3': Value('float64'), 'z_score_i1': Value('float64'), 'z_score_i2': Value('float64'), 'z_score_i3': Value('float64'), 'top1_iter': Value('float64'), 'top1_score': Value('float64'), 'top2_score': Value('float64'), 'top1_top2_gap': Value('float64'), 'unique_top1': Value('bool'), 'margin': Value('float64'), 'train_prior': Value('string'), 'fallback_triggered': Value('bool'), 'selected_iter': Value('string'), 'selected_correct': Value('int64'), 'representation_selected_iter': Value('string'), 'representation_selected_correct': Value('int64'), 'agreement_pattern': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1850, 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 4 new columns ({'train_acc_i2', 'prior_tie', 'train_acc_i1', 'train_acc_i3'}) and 26 missing columns ({'raw_score_i1', 'correct_i2', 'top2_score', 'top1_score', 'raw_score_i2', 'z_score_i3', 'representation_selected_iter', 'top1_top2_gap', 'z_score_i1', 'unique_top1', 'split_group_id', 'top1_iter', 'raw_score_i3', 'correct_i1', 'selected_iter', 'fallback_triggered', 'question_id', 'representation_selected_correct', 'benchmark', 'correct_i3', 'margin', 'method', 'agreement_pattern', 'candidate_group_id', 'z_score_i2', 'selected_correct'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/hpachdpii/capdyn_match_rzero_global_nested5/capdyn_match_contrast/results/smoke_test/selected_hyperparameters.csv (at revision e261e1075f0f0d76964194448afa9776468f1584), ['hf://datasets/hpachdpii/capdyn_match_rzero_global_nested5@e261e1075f0f0d76964194448afa9776468f1584/capdyn_match_contrast/results/smoke_test/oof_predictions.csv', 'hf://datasets/hpachdpii/capdyn_match_rzero_global_nested5@e261e1075f0f0d76964194448afa9776468f1584/capdyn_match_contrast/results/smoke_test/preds_seed0_outer0.csv', 'hf://datasets/hpachdpii/capdyn_match_rzero_global_nested5@e261e1075f0f0d76964194448afa9776468f1584/capdyn_match_contrast/results/smoke_test/prior_by_fold.csv', 'hf://datasets/hpachdpii/capdyn_match_rzero_global_nested5@e261e1075f0f0d76964194448afa9776468f1584/capdyn_match_contrast/results/smoke_test/selected_hyperparameters.csv']
              
              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)
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                  ~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                  ~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._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
              domain: string
              seed: int64
              outer_fold: int64
              train_acc_i1: double
              train_acc_i2: double
              train_acc_i3: double
              train_prior: string
              prior_tie: bool
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1221
              to
              {'method': Value('string'), 'question_id': Value('string'), 'split_group_id': Value('string'), 'candidate_group_id': Value('string'), 'benchmark': Value('string'), 'domain': Value('string'), 'seed': Value('int64'), 'outer_fold': Value('int64'), 'correct_i1': Value('int64'), 'correct_i2': Value('int64'), 'correct_i3': Value('int64'), 'raw_score_i1': Value('float64'), 'raw_score_i2': Value('float64'), 'raw_score_i3': Value('float64'), 'z_score_i1': Value('float64'), 'z_score_i2': Value('float64'), 'z_score_i3': Value('float64'), 'top1_iter': Value('float64'), 'top1_score': Value('float64'), 'top2_score': Value('float64'), 'top1_top2_gap': Value('float64'), 'unique_top1': Value('bool'), 'margin': Value('float64'), 'train_prior': Value('string'), 'fallback_triggered': Value('bool'), 'selected_iter': Value('string'), 'selected_correct': Value('int64'), 'representation_selected_iter': Value('string'), 'representation_selected_correct': Value('int64'), 'agreement_pattern': 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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method
string
question_id
string
split_group_id
string
candidate_group_id
string
benchmark
string
domain
string
seed
int64
outer_fold
int64
correct_i1
int64
correct_i2
int64
correct_i3
int64
raw_score_i1
null
raw_score_i2
null
raw_score_i3
null
z_score_i1
null
z_score_i2
null
z_score_i3
null
top1_iter
null
top1_score
null
top2_score
null
top1_top2_gap
null
unique_top1
null
margin
float64
train_prior
string
fallback_triggered
null
selected_iter
string
selected_correct
int64
representation_selected_iter
string
representation_selected_correct
int64
agreement_pattern
string
always_i1
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YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Global CapDyn-Match(R-Zero Iter1–Iter3,严格 nested 5-fold)

本目录是唯一允许写入的实验工作区。三个 iteration 的 evaluation JSON 与 checkpoint 只读。

方法

从已保存的 Iter1/Iter2/Iter3 回答中监督选择,不重新生成、不重新 self-evolution。

  • QA 表示:冻结 Iter2 encoder,对 (question, a_k) 取 layer 23、最后一个非 padding token
  • f_QA:StandardScaler → TruncatedSVD(≤128) → balanced LogisticRegression
  • f_cap:Question TF-IDF/SVD ⊕ 精确 7 维 agreement
  • 融合:行内 z-score,搜索 alpha ∈ {0.25,0.50,0.75}、margin ∈ {0,0.05,0.10}
  • 切分单位:split_group_id = benchmark + question_id(AMC/AIME 的 32 个 rollout 不跨 fold)

路径

角色 路径
Iter1 eval /root/rzero_storage/evaluation/_root_rzero_storage_models_qwen3-4b_solver_v1_global_step_15_actor_huggingface
Iter2 eval /root/rzero_storage/evaluation/_root_rzero_storage_models_qwen3-4b_solver_v2_global_step_15_actor_huggingface
Iter3 eval /root/rzero_storage/evaluation/_root_rzero_storage_models_qwen3-4b_solver_v3_global_step_15_actor_huggingface
Iter2 encoder /root/rzero_storage/models/qwen3-4b_solver_v2/global_step_15/actor/huggingface

运行(必须使用 conda env r-zero)

conda activate r-zero
pytest -q tests
bash scripts/run_smoke.sh
python scripts/extract_iter2_qa_hidden.py --config configs/extract_hidden.yaml
bash scripts/run_full_nested5.sh

正式实验通过 tmux session rzero_capdyn_nested5 启动。

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