Summary of Self-play with Adversarial Critic: Provable and Scalable Offline Alignment For Language Models, by Xiang Ji et al.
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models
by Xiang Ji, Sanjeev Kulkarni, Mengdi Wang, Tengyang Xie
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed SPAC method uses reinforcement learning from human feedback to align large language models (LLMs) with offline preference data. It addresses the gap between popular preference optimization methods that show good empirical performance but lack theoretical guarantees and theoretically motivated methods that are computationally inefficient for large-scale applications like LLM alignment. The SPAC approach combines self-play and on-average pessimism techniques from the offline RL literature to achieve provable convergence under single-policy concentrability in the general function approximation setting. Empirically, it demonstrates competitive performance for LLM alignment on a 7B Mistral model evaluated on the Open LLM Leaderboard. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make sure that big language models agree with what people like and dislike. They want to find a way to make this happen in a way that is guaranteed to work, not just good sometimes. They came up with a new method called SPAC that uses a combination of ideas from other fields to achieve this goal. It’s the first approach that can both work well and be proven to do so. The paper also tested their idea on a big language model and showed it performed as well as other methods in some cases. |
Keywords
» Artificial intelligence » Alignment » Language model » Optimization » Reinforcement learning from human feedback
