mlsanityData to 7 Oct 2026

Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models

arXiv 2502.17387 ↗First posted 24 Feb 2025cs.LG cs.AI cs.CLcited by 104

On the map: LLM reasoning → Reinforcement learning for reasoning

Abstract (arXiv)

Increasing interest in reasoning models has led math to become a prominent testing ground for algorithmic and methodological improvements. However, existing open math datasets either contain a small collection of high-quality, human-written problems or a large corpus of machine-generated problems of uncertain quality, forcing researchers to choose between quality and quantity. In this work, we present Big-Math, a dataset of over 250,000 high-quality math questions with verifiable answers, purposefully made for reinforcement learning (RL). To create Big-Math, we rigorously filter, clean, and curate openly available datasets, extracting questions that satisfy our three desiderata: (1) problems with uniquely verifiable solutions, (2) problems that are open-ended, (3) and problems with a closed-form solution. To ensure the quality of Big-Math, we manually verify each step in our filtering process. Based on the findings from our filtering process, we introduce 47,000 new questions with verified answers, Big-Math-Reformulated: closed-ended questions (i.e. multiple choice questions) that have been reformulated as open-ended questions through a systematic reformulation algorithm. Compared to the most commonly used existing open-source datasets for math reasoning, GSM8k and MATH, Big-Math is an order of magnitude larger, while our rigorous filtering ensures that we maintain the questions most suitable for RL. We also provide a rigorous analysis of the dataset, finding that Big-Math contains a high degree of diversity across problem domains, and incorporates a wide range of problem difficulties, enabling a wide range of downstream uses for models of varying capabilities and training requirements. By bridging the gap between data quality and quantity, Big-Math establish a robust foundation for advancing reasoning in LLMs.

Key facts. Cited by 104 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for reasoning, one of 17 directions in LLM reasoning. Builds on 29 of its 36 references that are in the map. First posted on arXiv 24 Feb 2025.

Builds on

29 of 36 references.

Closest ancestors · 4

Same direction, newest first

  1. 2024Free Process Rewards without Process Labels2 Dec
  2. 2024Generative Verifiers: Reward Modeling as Next-Token Prediction27 Aug
  3. 2024GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements13 Feb
  4. 2023Let's Verify Step by Step31 May

Other references · 25

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  3. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,203
  4. 2025s1: Simple test-time scalingcited by 1,311
  5. 2016Bag of Tricks for Efficient Text Classificationcited by 869
  6. 2024GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Modelscited by 533
  7. 2023Are Emergent Abilities of Large Language Models a Mirage?cited by 515
  8. 2025Demystifying Long Chain-of-Thought Reasoning in LLMscited by 323
  9. 2025rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinkingcited by 308
  10. 2023SemDeDup: Data-efficient learning at web-scale through semantic deduplicationcited by 295
  11. 2018Learning Word Vectors for 157 Languagescited by 279
  12. 2020Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Designcited by 260
13 more
  1. 2024A Survey on Data Selection for Language Modelscited by 230
  2. 2020Prioritized Level Replaycited by 190
  3. 2024Orca-Math: Unlocking the potential of SLMs in Grade School Mathcited by 151
  4. 2025LLMs Can Easily Learn to Reason from Demonstrations Structure, not content, is what matters!cited by 132
  5. 2025Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thoughtcited by 108
  6. 2025Scaling Test-Time Compute Without Verification or RL is Suboptimalcited by 82
  7. 2025NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questionscited by 81
  8. 2023Efficient Online Data Mixing For Language Model Pre-Trainingcited by 77
  9. 2025T1: Advancing Language Model Reasoning through Reinforcement Learning and Inference Scalingcited by 77
  10. 2024Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Modelscited by 31
  11. 2024HARP: A challenging human-annotated math reasoning benchmarkcited by 25
  12. 2025Metastable Dynamics of Chain-of-Thought Reasoning: Provable Benefits of Search, RL and Distillationcited by 25
  13. 2025Examining False Positives under Inference Scaling for Mathematical Reasoningcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 52; 2026: 52.

104 papers.

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  1. 2025DeepMath-103K: A Large-Scale, Challenging, Decontaminated, and Verifiable Mathematical Dataset for Advancing Reasoningcited by 261
  2. 2025Light-R1: Curriculum SFT, DPO and RL for Long COT from Scratch and Beyondcited by 175
  3. 2025A Survey of Reinforcement Learning for Large Reasoning Modelscited by 175
  4. 2025Beyond Binary Rewards: Training LMs to Reason About Their Uncertaintycited by 107
  5. 2025Seed-Prover: Deep and Broad Reasoning for Automated Theorem Provingcited by 96
  6. 2025Stabilizing MoE Reinforcement Learning by Aligning Training and Inference Routerscited by 67
  7. 2025Can Large Reasoning Models Self-Train?cited by 66
  8. 2026MOPD: Multi-Teacher On-Policy Distillation for Capability Integration in LLM Post-Trainingcited by 60
  9. 2025Enigmata: Scaling Logical Reasoning in Large Language Models with Synthetic Verifiable Puzzlescited by 53
  10. 2025Advancing Multimodal Reasoning: From Optimized Cold Start to Staged Reinforcement Learningcited by 51
  11. 2025Reward Reasoning Modelcited by 47
  12. 2025How Far are VLMs from Visual Spatial Intelligence? A Benchmark-Driven Perspectivecited by 41
38 more
  1. 2025Diversity-Aware Policy Optimization for Large Language Model Reasoningcited by 39
  2. 2025SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoningcited by 39
  3. 2025Seed-Prover 1.5: Mastering Undergraduate-Level Theorem Proving via Learning from Experiencecited by 36
  4. 2025100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Modelscited by 29
  5. 2025The Open Proof Corpus: A Large-Scale Study of LLM-Generated Mathematical Proofscited by 27
  6. 2025Is It Thinking or Cheating? Detecting Implicit Reward Hacking by Measuring Reasoning Effortcited by 25
  7. 2026How do LLMs Compute Verbal Confidencecited by 23
  8. 2025Hybrid Reinforcement: When Reward Is Sparse, It's Better to Be Densecited by 19
  9. 2025ReForm: Reflective Autoformalization with Prospective Bounded Sequence Optimizationcited by 18
  10. 2025DeepDistill: Enhancing LLM Reasoning Capabilities via Large-Scale Difficulty-Graded Data Trainingcited by 17
  11. 2025Language Models Can Learn from Verbal Feedback Without Scalar Rewardscited by 17
  12. 2026PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoningcited by 17
  13. 2025ARES: Multimodal Adaptive Reasoning via Difficulty-Aware Token-Level Entropy Shapingcited by 16
  14. 2025From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learningcited by 15
  15. 2025OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Valuecited by 15
  16. 2025GeoGramBench: Benchmarking the Geometric Program Reasoning in Modern LLMscited by 13
  17. 2025QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMscited by 13
  18. 2025MiroMind-M1: An Open-Source Advancement in Mathematical Reasoning via Context-Aware Multi-Stage Policy Optimizationcited by 12
  19. 2025BOTS: A Unified Framework for Bayesian Online Task Selection in LLM Reinforcement Finetuningcited by 12
  20. 2025NP-Engine: Empowering Optimization Reasoning in Large Language Models with Verifiable Synthetic NP Problemscited by 11
  21. 2025Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMscited by 10
  22. 2025From Data-Centric to Sample-Centric: Enhancing LLM Reasoning via Progressive Optimizationcited by 10
  23. 2025EngiBench: A Benchmark for Evaluating Large Language Models on Engineering Problem Solvingcited by 10
  24. 2025Scaling Code-Assisted Chain-of-Thoughts and Instructions for Model Reasoningcited by 10
  25. 2025MATH-Beyond: A Benchmark for RL to Expand Beyond the Base Modelcited by 10
  26. 2025DiRL: An Efficient Post-Training Framework for Diffusion Language Modelscited by 10
  27. 2026GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learningcited by 10
  28. 2026Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Textcited by 9
  29. 2026To Mix or To Merge: Toward Multi-Domain Reinforcement Learning for Large Language Modelscited by 9
  30. 2026EvoTrainer: Co-Evolving LLM Policies and Training Harnesses for Autonomous Agentic Reinforcement Learningcited by 9
  31. 2025Beyond Markovian: Reflective Exploration via Bayes-Adaptive RL for LLM Reasoningcited by 8
  32. 2025VAR-MATH: Probing True Mathematical Reasoning in LLMS via Symbolic Multi-Instance Benchmarkscited by 8
  33. 2025DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimizationcited by 7
  34. 2026Reward Modeling from Natural Language Human Feedbackcited by 7
  35. 2026Analyzing and Improving Chain-of-Thought Monitorability Through Information Theorycited by 7
  36. 2026R2-dLLM: Accelerating Diffusion Large Language Models via Spatio-Temporal Redundancy Reductioncited by 6
  37. 2025Efficient PRM Training Data Synthesis via Formal Verificationcited by 5
  38. 2025Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?cited by 5

50 most cited of 104 All 104 (JSON)

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  2. 2025Reinforcement Learning for Reasoning in Large Language Models with One Training Example
  3. 2025JT-Math: A Multi-Stage Framework for Advanced Mathematical Reasoning in Large Language Models
  4. 2025Scaling Up RL: Unlocking Diverse Reasoning in LLMs via Prolonged Training
  5. 2025Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning
  6. 2025Confucius3-Math: A Lightweight High-Performance Reasoning LLM for Chinese K-12 Mathematics Learning
  7. 2025AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning
  8. 2025MathSmith: Towards Extremely Hard Mathematical Reasoning by Forging Synthetic Problems with a Reinforced Policy