Summary of Codepmp: Scalable Preference Model Pretraining For Large Language Model Reasoning, by Huimu Yu et al.
CodePMP: Scalable Preference Model Pretraining for Large Language Model Reasoning
by Huimu Yu, Xing Wu, Weidong Yin, Debing Zhang, Songlin Hu
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 abstract proposes a new approach to enhancing large language models’ (LLMs) reasoning abilities through reinforcement learning from human feedback (RLHF). The main challenge is the scarcity of high-quality preference data, which requires labor-intensive annotation. To address this issue, the authors introduce CodePMP, a scalable preference model pretraining pipeline that utilizes a large corpus of synthesized code-preference pairs from publicly available source code. This approach improves the efficiency of reward model finetuning and leads to significant improvements in LLMs’ reasoning performance on mathematical and logical reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to make large language models better at making decisions by learning from people’s preferences. The challenge is that it takes a lot of work to create the data needed for this process, but the authors have come up with a solution. They’ve created a system called CodePMP that can learn from lots of fake code-preference pairs and then use this knowledge to help improve the models’ decision-making abilities. This has led to some exciting results in areas like math and logic. |
Keywords
» Artificial intelligence » Pretraining » Reinforcement learning from human feedback » Rlhf
