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

arXiv: https://arxiv.org/abs/2509.21613 · HTML: https://mlsanity.com/paper/2509.21613/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 25 Sep 2025.

## Builds on (most cited first, 13 of 13)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback](https://mlsanity.com/paper/2309.00267/) (2023)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://mlsanity.com/paper/2306.04488/) (2023)
- [A Survey on Mixture of Experts in Large Language Models](https://mlsanity.com/paper/2407.06204/) (2024)
- [Decoding-Time Language Model Alignment with Multiple Objectives](https://mlsanity.com/paper/2406.18853/) (2024)
- [A Contextual Bandit Bake-off](https://mlsanity.com/paper/1802.04064/) (2018)
- [Multi-Objective Meta Learning](https://mlsanity.com/paper/2102.07121/) (2021)
- [Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization](https://mlsanity.com/paper/2310.15196/) (2023)
- [Ensemble Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.02618/) (2023)
- [Dynamic Reward Adjustment in Multi-Reward Reinforcement Learning for Counselor Reflection Generation](https://mlsanity.com/paper/2403.13578/) (2024)
- [Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback](https://mlsanity.com/paper/2302.03805/) (2023)
- [EMORL: Ensemble Multi-Objective Reinforcement Learning for Efficient and Flexible LLM Fine-Tuning](https://mlsanity.com/paper/2505.02579/) (2025)

## Built on by (most cited first, 1 of 1)

- [Evolutionary Soups: Evolving Mixture-of-Experts for Multi-Objective LLM Alignment](https://mlsanity.com/paper/2608.29978/) (2026)

## Abstract (arXiv)

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.