Summary of Direct Multi-turn Preference Optimization For Language Agents, by Wentao Shi et al.
Direct Multi-Turn Preference Optimization for Language Agents
by Wentao Shi, Mengqi Yuan, Junkang Wu, Qifan Wang, Fuli Feng
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 Direct Preference Optimization (DPO) method for adapting Large Language Models (LLMs) to agent tasks shows promise in alleviating compounding errors and optimizing Reinforcement Learning (RL) objectives. However, its application to multi-turn tasks is hindered by the inability to cancel partition functions. To overcome this limitation, a novel loss function called DMPO is introduced, combining state-action occupancy measures with length normalization from the Bradley-Terry model. Experimental results on three datasets demonstrate the effectiveness and superiority of DMPO for multi-turn agent tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) need to be adapted for agent tasks. A technique called Direct Preference Optimization (DPO) can help by fixing errors and optimizing learning objectives. But DPO doesn’t work well with complex conversations that involve multiple turns. To fix this, researchers created a new way of combining language models and conversation metrics. They tested it on three datasets and found it worked better than other methods. |
Keywords
» Artificial intelligence » Loss function » Optimization » Reinforcement learning
