---
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.