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AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling

Paper · 2025 · Zihan Liu, Yang Chen, Mohammad Shoeybi et al.

Summary

The paper tackles two linked problems: making open LLMs stronger at multi-step mathematical problem solving, and building reward models that can reliably pick the correct answer from several generated solutions. For the instruction models, the authors train in two stages. A general SFT stage (producing "AceInstruct") covers general, coding and math prompts. A math-focused SFT stage follows, using deduplicated open-source prompts, about one million synthetic prompts seeded from NuminaMath, and responses regenerated by GPT-4o-mini and Qwen2.5-Math-72B-Instruct. For verification, they introduce AceMath-RewardBench and study how pair construction, training objectives and stylistic biases affect math reward models. The resulting AceMath-72B-Instruct beats Qwen2.5-Math-72B-Instruct, GPT-4o and Claude-3.5 Sonnet on average. AceMath-72B-RM outperforms prior reward models, and pairing the two gives the best average rm@8 score. All weights, training data and the benchmark are released. This gives an open, reproducible recipe for math-specialized models, though they are limited to short chain-of-thought reasoning.

Key points

  • 7B results: AceMath-7B-Instruct averages 67.2 across seven math benchmarks, against 62.9 for Qwen2.5-Math-7B-Instruct. It nearly matches the ten-times-larger Qwen2.5-Math-72B-Instruct (68.2).
  • 72B results: AceMath-72B-Instruct averages 71.8, rising to 74.8 under rm@8 with AceMath-72B-RM.
  • SFT data: The math SFT set has roughly 2.3M samples (1.83B tokens), of which about 1.2M are reused in general SFT. The general SFT data also includes about 1.2M coding samples (0.67B tokens) and about 0.7M general-domain samples (0.55B tokens).
  • Reward model on AceMath-RewardBench: AceMath-72B-RM reaches 69.53 average rm@8, compared with 68.46 for Qwen2.5-Math-RM-72B, and wins on 6 of 7 datasets.
  • Reward model on RewardMATH: AceMath-72B-RM scores 68.94. Many general reward models exceed 95% on the RewardBench MATH500 split but fall to roughly 20–37% on RewardMATH.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Computer Science · 19,503 worksArtificial Intelligence · 18,805 worksArts and Humanities · 17,959 worksLanguage and Linguistics · 17,789 worksMathematics · 233 worksComputational Theory and Mathematics · 496 worksApplied Mathematics · 73 works

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1,948 works

Cite

Cite as Zihan Liu, Yang Chen, Mohammad Shoeybi, Bryan Catanzaro and Wei Ping. 2025. AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling. Findings of the Association for Computational Linguistics: ACL 2025.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2025.findings-acl.206 · arXiv 2412.15084

@inproceedings{liu2025acemath,
  title = {{AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling}},
  author = {Zihan Liu, Yang Chen, Mohammad Shoeybi, Bryan Catanzaro and Wei Ping},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2025},
  year = {2025},
  doi = {10.18653/v1/2025.findings-acl.206},
  eprint = {2412.15084},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2025.findings-acl.206}
}

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