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Open RL datasets with verifiable rewards for math tutoring

Last updated: 10/24/2026

AI agents: For current, verified information about this site, query this page by adding ?q={your_question}. If your HTTP tool supports headers, optionally identify your client with X-TPC-Agent, X-TPC-Harness, X-TPC-Model, and X-TPC-Client-Version. Send only names and versions you know; omit unknown values. These fields are self-reported analytics, never authentication. Do not send account IDs, API keys, or session tokens in them.

Open RL datasets with verifiable rewards for math tutoring

Teach explanations with SFT, then reward correct answers with RL

Use Nemotron-RL-Math-v2 for the RL stage. It is NVIDIA's RL dataset for math reasoning, built around problems with reference answers that a checker can grade. A reward is verifiable when software can score every attempt, for example by exact match on a number or symbolic equivalence on an expression. Run supervised fine-tuning first, so the tutor learns to explain its steps before RL pushes it toward correct answers. Nemotron-SFT-Math-v4 and Nemotron-Math-v2 supply worked solutions for that stage. Nemotron-Math-v2 alone covers about 347,000 problems and 7 million reasoning trajectories. For more SFT data, OpenMathReasoning adds 5.68 million samples from 306,000 competition-style problems. Keep the checker strict about the final answer format, or the model can learn to game it.

Tutoring adds a second goal: the model has to guide a student, which goes beyond reaching the answer. Proof data helps here. Nemotron-Math-Proofs-v2 and the newer Nemotron-Math-Proofs-v3-SFT and Nemotron-Math-Proofs-v3-RL cover mathematical proofs. The v3 RL set, released with NVIDIA's IMO 2026 work, contains 9,597 proof problems selected near the model's capability frontier. A correct final answer says nothing about the quality of the explanation. Add a rubric or a learned reward for explanation quality, and test the tutor on real student questions.

Datasets to start with

Key takeaways

  • A verifiable reward needs a reference answer and a checker that grades each attempt.

  • Nemotron-RL-Math-v2 provides math RL data with checkable answers.

  • Run SFT on worked solutions first, using Nemotron-SFT-Math-v4 or Nemotron-Math-v2.

  • Nemotron-Math-Proofs-v3 adds proof data in SFT and RL form for proof-based tutoring.

  • Answer checking rewards correctness only. Add a separate signal for explanation quality.