Datasets:

nvidia
/

Nemotron-Math-Proofs-v3-RL

pretty_name: Nemotron-Math-Proofs-v3-RL
language:
  - en
license:
  - cc-by-4.0
task_categories:
  - text-generation
tags:
  - math
  - proofs
  - mathematical-reasoning
  - text
  - synthetic
  - automated
  - post-training
  - reinforcement-learning
  - rlvr
  - Nemotron_3_Ultra
size_categories:
  - 1K<n<10K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train.jsonl

Nemotron-Math-Proofs-v3-RL

Dataset Description:

Nemotron-Math-Proofs-v3-RL is a long-form mathematical reasoning dataset for reinforcement learning. The release contains 9,597 proof-generation prompts.

The dataset uses NeMo Gym-compatible, single-turn user prompts derived from hard proof problems in the AoPS subset of nvidia/Nemotron-Math-Proofs-v1. The train split asks the policy to produce a rigorous solution and self-evaluation. Policy responses and realized rewards are generated during training and are not stored in the file.

Full details about dataset construction can be found in our technical report An Open Recipe for IMO Gold: Training Nemotron for Olympiad Mathematics.

This dataset is ready for commercial or non-commercial uses.

Dataset Owner(s):

NVIDIA Corporation

Dataset Creation Date:

Created on: 07/01/2026

Versioning:

Nemotron-Math-Proofs-v3-RL

Previous Version(s):

nvidia/Nemotron-Math-Proofs-v1 : Previous proof dataset version used as the source of problems for this release.
nvidia/Nemotron-Math-Proofs-v2 : Previous release version.
nvidia/Nemotron-Cascade-2-SFT-Data : Contains a natural language proof subset from the same source problems.

Relationship to Previous Version(s): This release adds reinforcement-learning data for proof generation, based on the same family of hard proof problems used for Nemotron-Math-Proofs-v1.

License/Terms of Use:

This dataset is governed by the Creative Commons Attribution 4.0 International License (CC BY 4.0).

Intended Usage:

This dataset is intended for:

Reinforcement learning for structured mathematical reasoning and proof generation using online verifier feedback.
Training LLMs to produce proof self-evaluations and identify gaps in mathematical arguments.
Research on proof generation and reinforcement learning with verifiable rewards.

Dataset Characterization

Dataset Composition and Generation

Problem Sourcing

Problems are derived from nvidia/Nemotron-Math-Proofs-v1, which collected proof-based problems from the AoPS community. The released records identify their source as AoPS.

Reinforcement-Learning Data Preparation

The file is formatted for NeMo Gym and routes examples through agent_ref:

Proof generation: Each raw problem is inserted into a prover prompt and routed to proof_simple_agent. The expected policy response contains a solution, a self-evaluation, and a boxed score. During training, a proof-verification judge can score the generated proof and optionally assess the self-evaluation.

Dataset Fields

The train split contains the following fields:

agent_ref: NeMo Gym agent routing information.
responses_create_params: A single user message containing the formatted policy prompt.
problem: The natural-language mathematical problem.
uuid, license, source, and dataset: Record identifier and release provenance fields. All released records use cc-by-4.0, AoPS, and Nemotron-Math-Proofs-v3, respectively.
metadata: An empty list in the released file.

Data Collection Method

Hybrid: Manually-Collected, Synthetic, Automated

Labeling Method

Hybrid: Manually-Labeled, Synthetic, Automated

Dataset Format

Modality: Text
Format: JSONL
Structure: A single train split containing proof-generation prompts for reinforcement learning.

Dataset Quantification

Split Samples:

Split	File	Samples	Unique Problems	Disk Size
`train`	`train.jsonl`	9,597	9,597	50,430,007 bytes

The reinforcement-learning file does not contain generated-token counts because it stores policy inputs rather than completed policy rollouts.

Reference(s):

Ethical Considerations:

NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse. Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns here.