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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
prompt: string
completion: string
-- schema metadata --
huggingface: '{"info": {"features": {"prompt": {"dtype": "string", "_type' + 67
to
{'input_ids': List(Value('int32')), 'attention_mask': List(Value('int8')), 'labels': List(Value('int64'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 609, in wrapped
                  for item in generator(*args, **kwargs):
                              ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py", line 74, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/arrow/arrow.py", line 54, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              prompt: string
              completion: string
              -- schema metadata --
              huggingface: '{"info": {"features": {"prompt": {"dtype": "string", "_type' + 67
              to
              {'input_ids': List(Value('int32')), 'attention_mask': List(Value('int8')), 'labels': List(Value('int64'))}
              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 1342, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 907, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, 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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input_ids
list
attention_mask
list
labels
list
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End of preview.

YAML Metadata Warning:The task_categories "text2sql" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

MATS-SQL Bundle — Paper-Format Pipeline (iter1)

Full MATS multi-agent Text2SQL pipeline reproduced on BIRD-bench. Validators use the paper's Feedback: + Conclude: correct/incorrect. format (not wrapper-tag).

Final BIRD-dev numbers (1524/1534 questions with usable db_path)

Config planner@1 (T=0) pipeline@1 (T=0) oracle pass@8 trained selector EX
PLANNER-only 51.54% 51.54% 70.80%
SFT-VF (paper validators) 51.48% 52.20% 71.65% 59.91%
ORPO iter1 COLLAB 51.08% 51.81% 71.19% 59.97%
ORPO iter1 INDEP 51.74% 52.59% 71.95% 60.31%

All three pipelines converge to ~60% selector EX. Validator-internal ORPO gains (COLLAB reward-acc +10/+17pp over INDEP) do not translate to pipeline EX.

Layout

models/
  planner-3B-sft/                          # Qwen2.5-Coder-3B, planner SFT
  planner-3B-orpo-iter1/                   # planner ORPO iter1 (Alg.1)
  validator-sel-1B-sft-paper/              # Llama-3.2-1B, val-sel SFT (paper format)
  validator-sel-1B-orpo-iter1-collab-paper/
  validator-sel-1B-orpo-iter1-indep-paper/
  validator-cond-1B-sft-paper/             # Llama-3.2-1B, val-cond SFT (paper format)
  validator-cond-1B-orpo-iter1-collab-paper/
  validator-cond-1B-orpo-iter1-indep-paper/
  fixer-1B-sft/                            # Llama-3.2-1B, fixer SFT
  fixer-1B-orpo-iter1/                     # fixer ORPO iter1
  selector-3B-sft/                         # Qwen2.5-Coder-3B, selector SFT

data/
  hf_val_sel_paper_v1/                     # paper-format val-sel SFT data (8890 train + 468 test)
  hf_val_cond_paper_v1/                    # paper-format val-cond SFT data
  hf_orpo_val_{sel,cond}_paper_iter1_{collab,indep}/   # ORPO iter1 pairs
  planner_3B_greedy_bird_train.jsonl       # planner-3B greedy preds on BIRD-train (9360 q, 57.6% correct)

eval_results/
  paper_{SFT_VF,COLLAB_par,INDEP_par}_passAt8_bird_dev.jsonl  # K=8 T=1.0 full rollouts
  paper_greedy_{PLANNER_ONLY,SFT_VF,COLLAB,INDEP}_passAt1_bird_dev.jsonl  # T=0 K=1 rollouts

scripts/      # pipeline + builders + trainer + selector EX
recipes/iter1-paper/  # ORPO recipes for the 4 validator variants

HANDOFF_SELECTOR_TASK.md  # next-step doc: lift selector EX from 60% to 67%

Validator training reward accuracies (eval_dpo on test split)

Model eval_loss eval_rewards/accuracies
validator-sel-1B-orpo-iter1-collab-paper 0.174 69.7%
validator-sel-1B-orpo-iter1-indep-paper 0.210 59.7%
validator-cond-1B-orpo-iter1-collab-paper 0.148 89.7%
validator-cond-1B-orpo-iter1-indep-paper 0.163 72.0%

COLLAB > INDEP by +10pp (sel) and +17.7pp (cond) at the chosen-vs-rejected level, but the gain doesn't propagate to end-to-end EX. See HANDOFF_SELECTOR_TASK.md.

Hyperparameters

  • SFT (validator/fixer/selector): lr=2e-5 cosine, 2 epochs, bf16, completion-only loss.
  • ORPO (paper §4.3): β=0.1, lr=8e-6 (1e-6 for val-sel-indep due to NaN), max_steps=400, max_grad_norm=0.5.
  • Rollout (run_pipeline_rollouts.py): K=8 candidates per question, T=1.0, top_p=0.9.
  • Fixer gated by planner_exec_ok=False.

Reproduce

# 1. Generate paper-format validator SFT data (uses Qwen-2.5-72B as teacher)
python scripts/gen_planner_preds_for_validator.py --out data/planner_3B_greedy_bird_train.jsonl  # ~15min
python scripts/gen_validator_sft_qwen72b.py --input data/planner_3B_greedy_bird_train.jsonl     # ~7h

# 2. Train paper-format validators
python scripts/train_sft_completion_only.py --base meta-llama/Llama-3.2-1B-Instruct \
    --data data/hf_val_sel_paper_v1 --chat_format llama3 --epochs 2 --lr 2e-5

# 3. Generate ORPO iter1 data (collab + indep)
python scripts/build_orpo_data.py --agent validator_sel --mode collab --K 4 --temperature 1.0 --max_questions 2000

# 4. ORPO train (per recipe in recipes/iter1-paper/)
accelerate launch alignment-handbook/scripts/run_orpo.py recipes/iter1-paper/orpo-val-sel-collab-paper.yaml

# 5. K=8 rollout eval on BIRD-dev
python scripts/run_pipeline_rollouts.py --input data/sft_bird_with_evidence_dev_text2sql.json \
    --K 8 --temperature 1.0 ...

# 6. Selector EX
python scripts/compute_bestofn_with_selector.py eval_results/paper_*_passAt8_bird_dev.jsonl

Open problem — see HANDOFF_SELECTOR_TASK.md

Selector EX is saturated at ~60% across all three validator regimes. Target is ≥67%. Oracle pass@8 ≈ 72% gives 7pp headroom; selector picks 60/72 = 83% of available correct. Next agent should focus on lifting selector pick-rate (or oracle@8 ceiling), not validators.

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