| --- |
| language: |
| - en |
| task_categories: |
| - text-generation |
| tags: |
| - math |
| - reasoning |
| - evaluation |
| - decomposition |
| configs: |
| - config_name: math5000 |
| data_files: |
| - split: test |
| path: data/math5000/*.parquet |
| - config_name: amc23 |
| data_files: |
| - split: test |
| path: data/amc23/*.parquet |
| - config_name: aime24 |
| data_files: |
| - split: test |
| path: data/aime24/*.parquet |
| - config_name: aime25 |
| data_files: |
| - split: test |
| path: data/aime25/*.parquet |
| --- |
| # Math recursive evaluation traces |
|
|
| Full saved evaluation trajectories for GRPO-BINARY and GRPO-TW at fixed step-100 checkpoints. **158,400 rollouts**, including all successes and failures from the original evaluation and three additional repeats. |
|
|
| ## Browse |
|
|
| Select a benchmark configuration (`math5000`, `amc23`, `aime24`, `aime25`). Every configuration has a `test` split and the same columns. One row is one complete rollout, not one child call and not an average. |
|
|
| | Benchmark | Questions | Samples per question per run/model/method | Rollout rows | |
| |---|---:|---:|---:| |
| | math5000 | 5000 | 1 | 120,000 | |
| | amc23 | 40 | 16 | 15,360 | |
| | aime24 | 30 | 16 | 11,520 | |
| | aime25 | 30 | 16 | 11,520 | |
|
|
| ## Columns |
|
|
| - `question`, `reference_answer`: exact frozen evaluation inputs, including documented repairs. |
| - `model_output`: all complete call outputs in recorded order, with call identifiers and roles. No summaries or truncation in the stored data. |
| - `trajectory`: structured list containing every saved call field, including complete `input_text`, `output_text`, parent IDs, aggregation links, token counts, and parse status. Recorded order is not a claim that parallel calls executed sequentially. |
| - `decoding_method`: `free_choice`, `forced_direct`, or `forced_decomposition`; the intervention applies at the root. `root_action` records the actual root action. Recursive child behavior remains visible in the trajectory. |
| - `model` / `model_id`, `checkpoint_step`, `checkpoint_manifest_sha256`: model identity; this digest identifies the checkpoint manifest, not a direct hash of the weights. |
| - `run`: `original`, `repeat_1`, `repeat_2`, or `repeat_3`. `repeat=0` denotes the original. `sample_index` is zero based. |
| - `temperature`: 0 for MATH5000 (one greedy answer); 0.5 for AMC/AIME (16 samples per question). All use top-p 1 and top-k 0. |
| - `base_seed`, `cell_seed`, `shard_id`: recorded run and execution-cell seeds. These are not independently assigned per-rollout seeds. |
| - `answer_correct`: audited final-answer equivalence; `format_valid`: saved protocol validity; `strict_correct`: both. The reported evaluation table uses strict correctness. |
| - `original_grading_json`, `audited_grading_json`, `adjudication_json`, `grading_method`, `reviewer`: preserve parser results and applied exact prediction/reference-pair review decisions. Codex review is AI review, not human annotation. |
| - `source_file`, `source_line`, hashes, and dataset revision fields: immutable source provenance. Source paths are repository-relative. |
|
|
| ## Reproduce the three-repeat evaluation |
|
|
| Filter to `repeat` in `[1, 2, 3]`; the original is retained for provenance and is excluded from the reported three-repeat mean and sample SD. For each run/model/method, mean@k is the mean of `strict_correct`; pass@16 is the fraction of questions with at least one strictly correct answer among that run’s 16 samples. Compute per-run metrics first and then average the three runs. Pooling 48 samples would be a different metric. |
|
|
| ```python |
| from datasets import load_dataset |
| ds = load_dataset("parksoojae/math-recursive-eval-traces", "math5000", split="test", token=True) |
| repeats = ds.filter(lambda row: row["repeat"] in (1, 2, 3)) |
| ``` |
|
|
| ## Provenance and limitations |
|
|
| The export was read back from Parquet and checked against all 96 audited run/model/method/benchmark cells, including answer-only and strict mean/pass metrics (384 comparisons). Every trajectory was round-trip checked. No new inference, regrading, checkpoint upload, or modification of frozen artifacts occurred. |
|
|
| These are repeats of inference with fixed trained checkpoints, not independent training seeds. Correct final answers do not certify correct reasoning. Frozen grading is retained, including known limitations; later qualitative failure-audit flags do not silently override benchmark grades. This release does not include the subjective failure-category labels as benchmark truth. |
|
|
| See `provenance/` for input repairs, source hashes, exact-pair adjudications, per-run score tables, and validation results. |
|
|
| ## Upstream benchmark sources |
|
|
| - **math5000**: [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval/tree/0530c78699ea5e8eb5530600900e1f328b48acad), test split, revision `0530c78699ea5e8eb5530600900e1f328b48acad`. |
| - **amc23**: [math-ai/amc23](https://huggingface.co/datasets/math-ai/amc23/tree/80815d37005feb82cd7f8fbc6901d5d3eff43057), test split, revision `80815d37005feb82cd7f8fbc6901d5d3eff43057`. |
| - **aime24**: [math-ai/aime24](https://huggingface.co/datasets/math-ai/aime24/tree/83a7f387baaa524a8bda0022eac0541582297103), test split, revision `83a7f387baaa524a8bda0022eac0541582297103`. |
| - **aime25**: [math-ai/aime25](https://huggingface.co/datasets/math-ai/aime25/tree/563bb8404243c5f09de6ec262f2db674fe5bce9b), test split, revision `563bb8404243c5f09de6ec262f2db674fe5bce9b`. |
|
|
| Questions and reference answers retain their upstream rights and terms; this private export does not grant a new blanket license to the combined material. Upstream provenance is recorded rather than assigning an unsupported aggregate license. |
|
|
