Summary of Self-augmented Preference Optimization: Off-policy Paradigms For Language Model Alignment, by Yueqin Yin and Zhendong Wang and Yujia Xie and Weizhu Chen and Mingyuan Zhou
Self-Augmented Preference Optimization: Off-Policy Paradigms for Language Model Alignment
by Yueqin Yin, Zhendong Wang, Yujia Xie, Weizhu Chen, Mingyuan Zhou
First submitted to arxiv on: 31 May 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 paper introduces Self-Augmented Preference Optimization (SAPO), a scalable and effective training paradigm that doesn’t require pre-collected paired preference data. Building on self-play, SAPO incorporates an off-policy learning pipeline with an Exponential Moving Average model and replay buffer to dynamically update response segments. This allows for real-time feedback integration with historical insights. Evaluations of LLaMA3-8B and Mistral-7B models across benchmarks (Open LLM Leaderboard, IFEval, AlpacaEval 2.0, MT-Bench) show SAPO matching or surpassing offline contrastive baselines like DPO and Odds Ratio Preference Optimization, outperforming offline self-play methods like SPIN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s main idea is to create a new way to train language models that doesn’t need pre-collected data. They call this method Self-Augmented Preference Optimization (SAPO). It uses ideas from another technique called self-play and adds some extra features to make it more powerful. The authors tested SAPO on two different models and showed that it works well on many benchmark tests. |
Keywords
» Artificial intelligence » Optimization
