license: mit
task_categories:
- text-generation
language:
- en
tags:
- rl
- confidence-estimation
- calibration
- math
- code
size_categories:
- n<1K
configs:
- config_name: default
data_files:
- split: test
path: data/test-*.parquet
rl-value-eval
Held-out evaluation set (774 prompts) for the Block-3 confidence / correctness-estimation
comparison. Pairs with YangyiYY/rl-value-confidence-train
(training) and the nct-ppo actor/critic models. Each row is in the verl RL schema so the shared
grader scores it directly.
Single test split (the former validation+test splits are merged into one).
Composition & subset annotation
Every row carries a fine-grained subset label in both extra_info.sub_source and a top-level
sub_source column:
| data_source | n | sub_source = |
|---|---|---|
taco |
300 | TACO difficulty — EASY / MEDIUM / MEDIUM_HARD / HARD / VERY_HARD |
codeforces |
300 | rating_1300, rating_1400 |
math_box (numina) |
98 | NuminaMath origin — cn_k12 / synthetic_math / orca_math / olympiads / gsm8k / synthetic_amc / aops_forum / amc_aime / math |
codecontests |
76 | rating_800 … rating_1200 |
Grader / ground_truth
reward_model.ground_truth is built for the shared reference grader (reference/ in the
project repo): math → {"answer": …}; code → input_output JSON with the full hidden test
suites (CodeContests public+private+generated, Codeforces full suite, TACO raw
input_output incl. call-based fn_name), no per-problem test-case cap. Correctness =
last \boxed{} match (math) or all stdio/functional cases pass (code, 6s/case · 60s/problem).
Schema
data_source · prompt (chat messages) · ability · reward_model.ground_truth · sub_source ·
extra_info (id, rating, sub_source).