Summary of Tpo: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees, by Weibin Liao et al.
TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees
by Weibin Liao, Xu Chu, Yasha Wang
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 Tree Preference Optimization (TPO), a novel algorithm that enhances the long-chain reasoning capabilities of large language models (LLMs) by directly learning from preference trees. TPO addresses the limitations of existing Direct Preference Optimization (DPO) methods, which fail to learn multiple responses with varying degrees of preference/dispreference. The proposed approach formulates the language model alignment as a Preference List Ranking problem and utilizes Adaptive Step Reward to adjust reward values for fine-grained preference optimization. Experimental results on mathematical reasoning tasks demonstrate that TPO consistently outperforms DPO across five public LLMs on four datasets, highlighting its effectiveness in enhancing long-chain reasoning capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research improves how computers can reason and solve problems by creating a new way to teach them what’s important. The current method, called Direct Preference Optimization (DPO), is limited because it only looks at two options at a time. This new approach, Tree Preference Optimization (TPO), lets the computer learn from many possible answers and pick the best one. TPO also helps the computer understand which steps are most important in solving a problem. The results show that TPO works better than DPO on five different computer models and four problem sets. |
Keywords
» Artificial intelligence » Alignment » Language model » Optimization
