| --- |
| license: mit |
| task_categories: [text-generation] |
| language: [en] |
| tags: [rl, confidence-estimation, calibration, code, competitive-programming] |
| size_categories: [1K<n<10K] |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # rl-value-confidence-train-code |
|
|
| **Code-domain** training set (~4.9K) for a confidence / correctness estimator, sampled from the raw |
| competitive-programming datasets **Codeforces (MatrixStudio)**, **BAAI/TACO**, and |
| **deepmind/code_contests**. Pairs with |
| [`YangyiYY/rl-value-eval-code`](https://huggingface.co/datasets/YangyiYY/rl-value-eval-code) (eval) |
| and a ~25K RL-training split from the same pool (disjoint). |
| |
| Built by taking ALL distinct usable problems, **deduped by problem text GLOBALLY** (these datasets |
| heavily re-share Codeforces problems; small-source-first priority cf→cc→taco), split per source |
| 50:10:5 into train/conf/eval (**0 problem-text overlap** across splits). |
| |
| ## Grader / ground_truth |
| `data_source` is `codeforces` / `taco` / `codecontests` — each routes to the shared reference |
| grader (`reference/`, apps_execution) both for the RL reward and these labels, so they are IDENTICAL. |
| `reward_model.ground_truth` = `input_output` JSON with **the full hidden test suite, capped at 50 |
| test cases/problem** (problems whose gt still exceeds 1MB — a single giant test — are dropped, as |
| they can't be graded within the 60s/problem cap). |
| |
| ## Schema |
| `data_source` · `prompt=[{system}, {user: problem}]` · `ability="code"` · `reward_model.ground_truth` |
| · `sub_source` (codeforces/codecontests → rating band; taco → difficulty) · `extra_info` (id, rating, sub_source). |
| |
