| --- |
| license: mit |
| task_categories: |
| - text-generation |
| language: |
| - en |
| tags: |
| - rl |
| - confidence-estimation |
| - calibration |
| - math |
| - code |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-*.parquet |
| --- |
| |
| # rl-value-confidence-train |
|
|
| A held-out **training set for a confidence / correctness estimator** (~5.7k prompts), paired with |
| [`YangyiYY/rl-value-eval`](https://huggingface.co/datasets/YangyiYY/rl-value-eval) (evaluation) and |
| the [nct-ppo actor/critic](https://huggingface.co/YangyiYY/qwen2.5-7b-instruct-nct-ppo-actor) |
| models. Each row is a `(prompt, ground_truth)` in the exact verl RL schema, so the same rule-based |
| verifiers score it (math: `\boxed{}` grading; code: stdin/stdout or function test cases). |
|
|
| Built to be **disjoint from both** the RL training data and the calibration eval sets (enforced by |
| md5 of the normalized problem text — 0 overlap), at **matched difficulty** where the pools allow. |
|
|
| ## Composition (5728 rows) |
|
|
| | data_source | n | source | difficulty | |
| |---|---|---|---| |
| | `math_box` (numina) | 2000 | AI-MO/NuminaMath-CoT `train`, boxed answers | same distribution as RL training (sub-datasets: cn_k12, synthetic_math, olympiads, orca_math, …) | |
| | `taco` | 2998 | BAAI/TACO `train` | **sampled to match the RL-training taco difficulty mix** (EASY/MEDIUM/HARD/…) | |
| | `codecontests` | 655 | deepmind/code_contests (train+valid+test) | cf_rating **1201–1400** | |
| | `codeforces` | 75 | MatrixStudio/Codeforces-Python-Submissions | rating **1201–1400** | |
| |
| **Note on code difficulty:** the RL training + eval sets consumed *all* Codeforces/CodeContests |
| problems at rating ≤1200 (the training band). This held-out set therefore takes the **closest |
| available band (1201–1400)** for code — one notch above training — since ≤1200 has zero disjoint |
| problems left. numina and taco are at matched training difficulty. |
| |
| ## Schema |
| |
| `data_source` · `prompt` (list of `{role, content}` chat messages) · `ability` (`math`/`code`) · |
| `reward_model.ground_truth` (rule verifier target) · `extra_info` (`id`, `rating`, `difficulty`, |
| `sub_source`, `split="confidence_train"`). |
|
|
| ## Intended use |
|
|
| Train a correctness/confidence estimator (e.g. a value head or probe) on rollouts from the frozen |
| nct-ppo actor, then evaluate calibration (ECE/AUROC) on the disjoint `rl-value-eval` set. |
|
|
