MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models

MLREF introduces a Module Level Reward Evolution Framework that uses a persistent pool of reusable reward components to improve reward design in reinforcement learning. The framework constructs reward functions as linear combinations of modules from this pool and evolves the pool through reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback. Experiments on 17 tasks demonstrate that MLREF outperforms strong baselines, achieving a 25.2% improvement in locomotion and 6.6% in manipulation with more stable optimization dynamics.

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• llms
• reinforcement-learning
• robotics