Summary of Qwen2.5-math Technical Report: Toward Mathematical Expert Model Via Self-improvement, by An Yang et al.
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
by An Yang, Beichen Zhang, Binyuan Hui, Bofei Gao, Bowen Yu, Chengpeng Li, Dayiheng Liu, Jianhong Tu, Jingren Zhou, Junyang Lin, Keming Lu, Mingfeng Xue, Runji Lin, Tianyu Liu, Xingzhang Ren, Zhenru Zhang
First submitted to arxiv on: 18 Sep 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 The proposed series of math-specific large language models, Qwen2.5-Math and its variants, integrate self-improvement philosophy throughout the pipeline, from pre-training to inference. The core innovation lies in generating high-quality mathematical data through Qwen2-Math-Instruct during pre-training. A reward model (RM) is developed by sampling from Qwen2-Math-Instruct, applied iteratively for supervised fine-tuning (SFT), and ultimately guides the final SFT model’s reinforcement learning. The RM also optimizes the model’s performance at inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a series of math-specific large language models that can improve themselves. They start by generating high-quality math data, then use this data to train the models. The models are trained iteratively, with the reward model guiding each step. This leads to better models and ultimately optimizes their performance. |
Keywords
» Artificial intelligence » Fine tuning » Inference » Reinforcement learning » Supervised
