RSPO: Regularized Self-Play Alignment of Large Language Models
Quick summary
arXiv:2503.00030v3 Announce Type: replace-cross Abstract: Self-play-based policy optimization has emerged as an effective approach for fine-tuning large language models (LLMs), formulating preference optimization as a two-player game. However, the regularization with respect to the reference policy, which is crucial for mitigating over-optimization, has been insufficiently investigated in self-play alignment. To study the impact of different regularization strategies, we propose Regularized Self-Play Policy Optimization (RSPO), a novel framework that unifies prior methods and enables simple pl
Key takeaways
- arXiv:2503.00030v3 Announce Type: replace-cross Abstract: Self-play-based policy optimization has emerged as an effective approach for fine-tuning large language models (LLMs), formulating preference optimization as a two-player game.
- However, the regularization with respect to the reference policy, which is crucial for mitigating over-optimization, has been insufficiently investigated in self-play alignment.
- To study the impact of different regularization strategies, we propose Regularized Self-Play Policy Optimization (RSPO), a novel framework that unifies prior methods and enables simple pl
Why it matters
“RSPO: Regularized Self-Play Alignment of Large Language Models” may affect what data AI products can use and where accountability sits. Product teams should watch compliance duties, rights holders should watch enforcement, and users should watch transparency and appeal mechanisms.
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