In 2026, the best math datasets for AI combine massive scale from synthetic sources like Nemotron-Math-v2 and OpenMathInstruct-2 with the unbreakable verifiability of formal Lean4 proofs, where FormalMATH and CriticLeanBench from 2077AI x Abaka AI emerge as the premier benchmarks for reliable, hallucination-resistant theorem proving and critic-guided reasoning. Together, they drive frontier models toward genuine mathematical understanding, setting the highest standard for trustworthy evaluation and long-term progress.
Best Datasets for Math in 2026
In 2026, frontier AI models are pushing mathematical reasoning to new heights, with numerous state-of-the-art systems achieving over 90% accuracy on the challenging MATH benchmark's hardest problems and over 80% on recent AIME competitions. These leaps stem directly from access to massive, high-quality datasets that enable advanced chain-of-thought (CoT) reasoning, tool integration, and formal verification. In short, the best datasets for math in 2026 combine scale with verifiable CoT, tool integration, or formal proofs in Lean4.
This guide breaks down the top options into three practical categories: general benchmarks, large-scale synthetic training, and formal theorem proving. 2026 brings significantly larger synthetic datasets (e.g., Nemotron-Math-v2 with 7M+ traces) and new formal benchmarks from innovators like 2077AI.
What Makes a High-Quality Math Dataset?
Leading 2026 math datasets deliver:
- Massive scale (>1M examples) to provide meaningful gains in supervised fine-tuning and reasoning pretraining.
- Rich annotations including multi-trace chain-of-thought reasoning and tool-assisted problem-solving.
- Broad topic coverage spanning algebra, combinatorics, number theory, geometry, calculus, and multi-level difficulty from high-school to early undergraduate competitions.
- Strict verifiability with CAS-checked solutions or fully formalized Lean4 proofs, enabling machine-checkable correctness.
- Open licensing to ensure reproducibility and community adoption.
Today, synthetic and formal datasets form the backbone of math pretraining, while formal verification ensures models produce more reliable and verifiable proofs.
The defining feature of great datasets is not sheer size but verifiable structure that supports long-context reasoning and critic-guided problem solving, empowering AI models to achieve state-of-the-art mathematical performance.
Leading General Math Reasoning & Benchmark Datasets
These datasets remain fundamental for evaluating reasoning performance, even as models scale:
- GSM8K - The CoT Standard 8.5K diverse grade-school word problems
- MATH - Competition Gold Standard 12.5K graded problems from AMC/AIME. Top models now exceed 85% accuracy on the hardest levels.
- MathOdyssey - Expert-Level curated problems with detailed solutions, gaining traction for advanced evaluation.
Leading Large-Scale Training & Synthetic Datasets
Scale dominates training in 2026:
- OpenMathInstruct-2 (NVIDIA): 14M problem-solution pairs (from ~600K unique questions generated via Llama3.1-405B). Delivers 15.9% absolute gains on MATH (51.9% → 67.8%) when fine-tuning Llama-3.1-8B (Toshniwal et. al 2025).
- Nemotron-Math-v2 (NVIDIA): ~347K problems with 7M+ multi-mode traces and tool integration. Excels at long-context distillation and competition-level performance.
- ODA-Math-460k & OpenR1-Math-220k: Curated meso-scale alternatives (460K and 220K samples) with verified multi-trace solutions
In short, large-scale synthetic datasets like Nemotron-Math-v2 dominate training volume but curation is non-negotiable for usable quality in 2026 training pipelines.
Leading Formal & Theorem Proving Datasets (2026)
In 2026, formal verification with machine‑checked proofs is widely regarded as the gold standard for reliable math AI evaluation. Lean4‑verified problems eliminate ambiguity and hallucination risks that informal benchmarks can mask. Leading formal math datasets target verifiable Lean4 proofs and critic‑level reasoning capabilities:
- FormalMATH (2077AI x Abaka AI) – A large‑scale Lean4 benchmark of 5,560 formally verified Lean4 problems across algebra, number theory, calculus, and discrete math, extending from Olympiad‑level challenges to undergraduate theorems. Its high difficulty yields low success rates even for state‑of‑the‑art provers, making it a premier formal reasoning test.
- CriticLeanBench (2077AI x Abaka AI) – A 500 human-verified pairs evaluation set of natural language statements and Lean4 formalizations (250 correct, 250 incorrect), designed to train and assess critic‑guided reasoning that detects subtle semantic errors in informal‑to‑formal translation.
- LeanDojo / miniF2F – Foundational formal theorem‑proving resources. LeanDojo produces rich proof data by extracting thousands of theorems and tactics from Lean's mathlib, while miniF2F remains a widely used cross‑system benchmark linking natural language problems with Lean formalizations.
FormalMATH stands out as a high‑difficulty Lean4‑verified proving dataset, while CriticLeanBench uniquely enables robust critic training in semantic formalization - both driving advances in trustworthy theorem‑proving evaluation in 2026.
Looking Forward
In conclusion, 2026 represents a turning point in mathematical AI, where massive synthetic datasets like OpenMathInstruct-2 and Nemotron-Math-v2 provide the scale needed for breakthrough reasoning, while formal Lean4 benchmarks such as FormalMATH and CriticLeanBench ensure verifiable, hallucination-resistant progress.
By combining broad coverage, rich chain-of-thought annotations, and machine-checked proofs, these resources empower developers and researchers to train and evaluate models that approach human-expert performance across competition and theorem-proving domains. Whether you're fine-tuning frontier systems or pushing the boundaries of trustworthy AI, prioritizing these top datasets will be essential for staying at the forefront of math reasoning advancements in 2026 and beyond.
FAQ
- What are the main differences between synthetic and human-written math datasets in 2026? Synthetic sets offer unmatched scale and tool integration but require curation; human-written ones excel in verified difficulty and formality.
- How can I access FormalMATH or CriticLeanBench from 2077AI? Download directly from 2077AI datasets.
- Which dataset works best for training small models (<10B parameters)? Curated subsets of OpenMathInstruct-2 or ODA-Math provide efficient gains without massive compute.
- Why are Lean4-based formal datasets like FormalMATH gaining traction in 2026? They enable verifiable, reliable proving which is critical as informal benchmarks saturate.
Further Readings
Best Datasets for Math in 2025
Lean 4 Mathematical Formal Proofs Propel the Next Leap in AI Reasoning
How to Build Reliable IMO Math Datasets: Steps & Tips
Building High-Quality Reasoning Datasets for GenAI Models
The Most Comprehensive Sharing for Reasoning Dataset: CoT
Math Dataset: What It Is, Popular Examples, and How It's Used
