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

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Chenglin Liu, Xun Wang, Ruishuo Chen, Zhuoran Li, Longbo Huang
• Venue: arXiv: cs.LG
• Published: 2026-08-19
• Collected: 2026-08-20
• URL: http://arxiv.org/abs/2608.18827v1
• PDF: https://arxiv.org/pdf/2608.18827v1
• DOI: UNKNOWN
• Registry group: academic_apis_and_preprints
• Evidence role: canonical_academic_index
• Language: en
• Country / region: UNKNOWN

Plain-Language Brief

The research proposes a new framework called Module Level Reward Evolution Framework (MLREF) to address the issue of unreliable reward function preservation and reuse in reinforcement learning. MLREF uses a module pool, a repository of reusable reward components, and three mechanisms for evolution: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback. Experiments show that MLREF outperforms strong baselines in locomotion and manipulation tasks.

What the Document Says

• Reward function design is a challenge in reinforcement learning.
• Large language models (LLMs) can generate reward functions, but they produce them as monolithic programs which makes it hard to preserve and reuse effective components across iterations.
• The authors propose the Module Level Reward Evolution Framework (MLREF).
• MLREF consists of a module pool, a repository of reusable reward components that evolves over iterations by accumulating successful modules, refining underperforming ones, and reusing proven components.
• Reward functions are constructed as linear combinations of modules from the pool in MLREF.
• MLREF integrates three mechanisms: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback to improve reward optimization.
• Experiments on 17 tasks show that MLREF outperforms strong baselines by 25.2% in locomotion and 6.6% in manipulation.
• MLREF leads to more stable optimization dynamics compared to the baselines.

Key Concepts Mentioned

• reinforcement learning
• reward function design
• large language models
• module pool
• reusable reward components
• evolution
• reflection-based refinement
• hybrid credit assignment
• merge strategy with rollback

Claims Made by the Document

• Existing methods generate and revise reward functions as monolithic programs, making it difficult to preserve effective components.
• MLREF outperforms strong baselines by 25.2% in locomotion and 6.6% in manipulation.
• MLREF leads to more stable optimization dynamics compared to the baselines.

Evidence Used

• Experiments on 17 tasks

Methods / Approach

• Module Level Reward Evolution Framework (MLREF)
• reflection-based refinement
• hybrid credit assignment
• merge strategy with rollback

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The research does not discuss potential limitations of the proposed framework in detail.

Open Questions

• Potential long-term implications and challenges of the proposed framework.
• Scalability issues of the MLREF.

Notes for Future Q&A

May include interpretation. Labeled where it does.

• It would be interesting to explore the long-term implications and challenges of the proposed framework.
• Further studies could investigate the scalability of MLREF.

Raw Abstract / Excerpt

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 optimization object: the pool evolves across iterations by accumulating successful modules, refining underperforming ones, and reusing proven components; while reward functions are constructed as linear combinations of modules drawn from this pool. To drive this evolution, MLREF integrates three mechanisms: reflection-based refinement, hybrid credit assignment, and a merge strategy with rollback, which together improve the effectiveness and robustness of reward optimization. Experiments on 17 tasks show that MLREF outperforms strong baselines by 25.2% in locomotion and 6.6% in manipulation, with more stable optimization dynamics.