Summary of Evolving Alignment Via Asymmetric Self-play, by Ziyu Ye et al.
Evolving Alignment via Asymmetric Self-Play
by Ziyu Ye, Rishabh Agarwal, Tianqi Liu, Rishabh Joshi, Sarmishta Velury, Quoc V. Le, Qijun Tan, Yuan Liu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Data Analysis, Statistics and Probability (physics.data-an); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 framework for aligning large language models (LLMs) addresses the limitations of current RLHF frameworks by introducing a general open-ended approach that casts alignment as an asymmetric game between two players: a creator generating increasingly informative prompt distributions using reward signals, and a solver learning to produce more preferred responses on prompts produced by the creator. This framework, called Evolving Alignment via Asymmetric Self-Play (eva), outperforms state-of-the-art methods on widely-used benchmarks without requiring additional human-crafted prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models need to be aligned with human values and preferences. A new way to do this is proposed that lets a “creator” generate prompts for the model, and then the model learns to respond well to those prompts. This approach works better than other methods on benchmarks like Arena-Hard, and it doesn’t require any extra work from humans. |
Keywords
» Artificial intelligence » Alignment » Prompt » Rlhf
