Big-Math-RL-Verified

Big-Math-RL-Verified is a large bank of mathematics problems assembled by SynthLabs (published as SynthLabsAI on Hugging Face) and released in 2025 alongside the paper "Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models". It holds 251,122 problems, each filtered to meet three criteria: a uniquely verifiable answer, an open-ended (non multiple-choice) form, and a closed-form solution. Every row carries the problem text, the final answer, a source tag, a domain and an llama8b_solve_rate, the fraction of attempts a Llama 8B model got right, which acts as an empirical, per-problem difficulty score.

You access it directly on Hugging Face as Parquet, streamable through the datasets library with no account or application required, which is why the access tier is open. The filtering and reformulation code sits in the SynthLabsAI/big-math GitHub repository. Note that it provides final answers only, not full worked solutions, so it is a checking and grading resource rather than a source of modelled reasoning.

For RAG it is a supply of answer-checked practice items to retrieve against for maths tasks. For an AI tutor it is more pointed: the solve-rate lets you order practice by empirical difficulty and build a graded ladder rather than guessing at level, and because each answer is verifiable you can auto-mark a learner's attempt and drive mastery modelling without a human in the loop. The absence of worked solutions means you must supply the teaching yourself.

The dataset wrapper is licensed Apache 2.0, so commercial use is permitted with the usual notice retention. The important caveat is provenance: it aggregates other datasets (including MATH, GSM8k, Orca-Math, cn_k12, olympiads, aops_forum, HARP, Omni-MATH and amc_aime), whose own terms travel with the items, so the Apache licence does not cover the underlying content. Treat per-item rights as mixed in a commercial product.

It complements the worked-solution numinamath and the question-shaped stackmathqa, sits alongside amps, megamath, openthoughts3 and naturalproofs as reasoning-oriented maths data, and pairs well with structured curricula such as openstax, khan-academy, ck-12, siyavula and common-core when you need pedagogy rather than gradable practice items.