Summary of Acemath: Advancing Frontier Math Reasoning with Post-training and Reward Modeling, by Zihan Liu et al.
AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling
by Zihan Liu, Yang Chen, Mohammad Shoeybi, Bryan Catanzaro, Wei Ping
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces AceMath, a suite of math models that excel in solving complex math problems, along with highly effective reward models capable of evaluating generated solutions and reliably identifying the correct ones. The authors propose a supervised fine-tuning (SFT) process to develop instruction-tuned math models, achieving competitive performance across general domains before targeting the math domain. They also construct AceMath-RewardBench, a comprehensive benchmark for evaluating math reward models, and present a systematic approach to build their math reward models. The resulting model, AceMath-72B-RM, consistently outperforms state-of-the-art reward models when combined with AceMath-72B-Instruct, achieving the highest average rm@8 score across math reasoning benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a super smart math tool that can solve really hard math problems. The authors made a special way to train their math model, called AceMath, so it gets better at solving math problems. They also created a test to see how good their reward models are, and they made a new approach to build even better ones. This helps people who want to make AI better at math. |
Keywords
» Artificial intelligence » Fine tuning » Supervised
