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[2602.22146] Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual
Summary
This article presents a novel optimistic primal-dual framework for safe reinforcement learning from human feedback (RLHF) in large language models, ensuring last-iterate convergence and stability in practical applications.
Why It Matters
The research addresses critical challenges in aligning large language models with human preferences through reinforcement learning. By providing a stable and effective optimization framework, this work enhances the reliability of AI systems in real-world applications, contributing to safer AI development.
Key Takeaways
- Introduces a universal primal-dual framework for safe RLHF.
- Establishes last-iterate convergence guarantees for the proposed optimistic primal-dual algorithm.
- Highlights the importance of optimism in reducing oscillations in constrained alignment objectives.
- Unifies various existing alignment algorithms under a single theoretical framework.
- Addresses practical stability issues in reinforcement learning applications.
Computer Science > Machine Learning arXiv:2602.22146 (cs) [Submitted on 25 Feb 2026] Title:Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual Authors:Yining Li, Peizhong Ju, Ness Shroff View a PDF of the paper titled Provable Last-Iterate Convergence for Multi-Objective Safe LLM Alignment via Optimistic Primal-Dual, by Yining Li and 2 other authors View PDF Abstract:Reinforcement Learning from Human Feedback (RLHF) plays a significant role in aligning Large Language Models (LLMs) with human preferences. While RLHF with expected reward constraints can be formulated as a primal-dual optimization problem, standard primal-dual methods only guarantee convergence with a distributional policy where the saddle-point problem is in convex-concave form. Moreover, standard primal-dual methods may exhibit instability or divergence in the last iterate under policy parameterization in practical applications. In this work, we propose a universal primal-dual framework for safe RLHF that unifies a broad class of existing alignment algorithms, including safe-RLHF, one-shot, and multi-shot based methods. Building on this framework, we introduce an optimistic primal-dual (OPD) algorithm that incorporates predictive updates for both primal and dual variables to stabilize saddle-point dynamics. We establish last-iterate convergence guarantees for the proposed method, covering both exact policy optimization in the distributional space and convergen...
