MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models
Reward function design remains a bottleneck in reinforcement learning. While large language models (LLMs) have enabled automated reward generation, existing methods generate and revise reward functions as monolithic programs, making it difficult to reliably preserve and reuse effective components discovered in earlier iterations, leading to unstable performance across iterations. To address this, we propose Module Level Reward Evolution Framework (MLREF). At the core of MLREF is a module pool, a persistent repository of reusable reward components. MLREF treats the module pool as the primary op
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- FuzzyOverlapping authors or contributors · 62%ray-project/ray →
“Shared author/contributor keys: wang”
- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%bytedance/deer-flow →
“Shared author/contributor keys: wang”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “MLREF: Efficient Module Reuse for Reward Design in Reinforce” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Chenglin Liu →
“MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models”
- LinkedLinked via arxiv author · 85%Weixun Wang →
“MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models”
- LinkedLinked via arxiv author · 85%Ruishuo Chen →
“MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models”
- LinkedLinked via arxiv author · 85%Zhuoran Li →
“MLREF: Efficient Module Reuse for Reward Design in Reinforcement Learning via Large Language Models”