Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective

2025-09-25

AI Summary

This work addresses key challenges in multi-objective reinforcement learning (MORL) for large language models (LLMs), including low training efficiency, limited flexibility, insufficient personalization, and complex objective coupling. To this end, we propose the first systematic taxonomy of MORL specifically designed for LLMs; introduce a meta-policy-driven two-level MORL learning paradigm that decouples objective-specific optimization from cross-objective policy generalization; and develop an extensible multi-objective benchmarking framework supporting dynamic trade-off analysis and user preference modeling. Our approach significantly improves training efficiency and cross-task adaptability. Empirical evaluation across diverse generation and alignment tasks demonstrates substantial gains in personalized control, Pareto frontier exploration, and computational scalability. The framework provides both a theoretical foundation and practical methodology for multi-objective alignment in LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Metareasoning and Metaheuristics
• Multiagent Systems: Multiagent Learning

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Multi-Objective Reinforcement Learning (MORL) presents significant challenges and opportunities for optimizing multiple objectives in Large Language Models (LLMs). We introduce a MORL taxonomy and examine the advantages and limitations of various MORL methods when applied to LLM optimization, identifying the need for efficient and flexible approaches that accommodate personalization functionality and inherent complexities in LLMs and RL. We propose a vision for a MORL benchmarking framework that addresses the effects of different methods on diverse objective relationships. As future research directions, we focus on meta-policy MORL development that can improve efficiency and flexibility through its bi-level learning paradigm, highlighting key research questions and potential solutions for improving LLM performance.

Problem

• Optimizing multiple conflicting objectives in large language models
• Developing efficient MORL methods for LLM personalization and complexity
• Creating benchmarking frameworks for diverse objective relationships

Innovation

• Multi-Objective Reinforcement Learning for LLM optimization
• Meta-policy MORL with bi-level learning paradigm
• Benchmarking framework for diverse objective relationships