用多目标强化学习优化大模型，提升个性化与灵活性。
Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspective
- 提出多目标强化学习方法分类体系，分析其在大模型优化中的适用性。
- 构建面向多元目标关系的基准测试框架，评估不同方法效果差异。
- 展望元策略多目标学习，通过双层学习提升训练效率与适应性。
多目标强化学习（MORL）为大语言模型（LLMs）的多目标优化带来挑战与机遇。本文构建MORL分类体系，分析各类方法在LLM优化中的优势与局限，指出亟需高效灵活的方法以支持个性化功能及模型内在复杂性。提出一个面向多种目标关系的MORL基准框架，用于评估不同方法的影响。未来研究方向聚焦于元策略多目标学习，利用双层学习范式提升效率与灵活性，探讨关键科学问题与潜在解决方案，以进一步优化大模型性能。
原文摘要 · Abstract (English)
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.
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