Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning
Yuheng Zhang Dian Yu Baolin Peng Linfeng Song Ye Tian Mingyue Huo Nan Jiang Haitao Mi Dong Yu
Reinforcement Learning with Human Feedback (RLHF) has achieved great success
in aligning large language models (LLMs) with human preferences. Prevalent
RLHF approaches are reward-based, following the Bradley-Terry (BT) model assumption, which may not fully capture the complexity of human preferences. In
this paper, we explore RLHF under a general preference framework and approach
it from a game-theoretic perspective. Specifically, we formulate the problem as
a two-player game and propose a novel online algorithm, iterative Nash policy
optimization (INPO). The key idea is to let the policy play against itself via no-
regret learning, thereby approximating the Nash policy. Unlike previous methods,
INPO bypasses the need for estimating the expected win rate for individual responses, which typically incurs high computational or annotation costs. Instead,
we introduce a new loss objective that is directly minimized over a preference
dataset. We provide theoretical analysis for our approach and demonstrate its
effectiveness through experiments on various representative benchmarks. With an
LLaMA-3-8B-based SFT model, INPO achieves a 42.6% length-controlled win
rate on AlpacaEval 2.0 and a 37.8% win rate on Arena-Hard, showing substantial
improvement over the state-of-the-art online RLHF algorithms.
RLHF Theory, LLM Alignment