Research Paper#Large Language Models (LLMs), Reinforcement Learning, Preference Learning🔬 ResearchAnalyzed: Jan 3, 2026 08:40
Unregularized Linear Convergence in Zero-Sum Game for LLM Alignment
Published:Dec 31, 2025 12:08
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of aligning large language models (LLMs) with human preferences, moving beyond the limitations of traditional methods that assume transitive preferences. It introduces a novel approach using Nash learning from human feedback (NLHF) and provides the first convergence guarantee for the Optimistic Multiplicative Weights Update (OMWU) algorithm in this context. The key contribution is achieving linear convergence without regularization, which avoids bias and improves the accuracy of the duality gap calculation. This is particularly significant because it doesn't require the assumption of NE uniqueness, and it identifies a novel marginal convergence behavior, leading to better instance-dependent constant dependence. The work's experimental validation further strengthens its potential for LLM applications.
Key Takeaways
- •Addresses the limitations of traditional preference modeling in LLM alignment.
- •Introduces Nash learning from human feedback (NLHF) as a solution.
- •Provides the first convergence guarantee for OMWU in NLHF.
- •Achieves linear convergence without regularization, avoiding bias.
- •Demonstrates improved instance-dependent constant dependence.
- •Experimentally validated for both tabular and neural policy classes.
Reference
“The paper provides the first convergence guarantee for Optimistic Multiplicative Weights Update (OMWU) in NLHF, showing that it achieves last-iterate linear convergence after a burn-in phase whenever an NE with full support exists.”