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 (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).