Datasets:

YangyiYY
/

rl-value-eval-math

license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - rl
  - confidence-estimation
  - calibration
  - math
  - numina
  - aime
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*.parquet

rl-value-eval-math

Math-domain evaluation set (~5.1K) for confidence / correctness estimation. Pairs with YangyiYY/rl-value-confidence-train-math (confidence-train) and a 50K RL-training split (disjoint).

Bulk of the set is sampled ~uniformly across the 6 numina sub-sources (from PRIME-RL/Eurus-2-RL-Data), deduplicated by problem text and disjoint from the RL-train / confidence-train splits. It also includes two held-out competition sets:

AIME 2024 (30 problems, math-ai/aime24)
AIME 2025 (30 problems, math-ai/aime25)

Composition (`sub_source`, 5058 rows)

sub_source	n	source
synthetic_math / cn_k12 / olympiads / synthetic_amc	965 each	numina
aops_forum	949	numina
amc_aime	189	numina
aime24	30	math-ai/aime24
aime25	30	math-ai/aime25

Schema (our recipe)

data_source="math_box" for all rows (routes to the shared math grader) · prompt=[{system: "Please reason step by step, and put your final answer within \boxed{}."}, {user: <problem>}] · ability="math" · reward_model.ground_truth (numina answer / AIME integer) · sub_source (numina origin or aime24/aime25) · extra_info (id, sub_source, split).

rl-value-eval-math

Composition (sub_source, 5058 rows)

Schema (our recipe)

Composition (`sub_source`, 5058 rows)