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Life sciences · Preprint
arXiv · August 19, 2026
Early or partial results. Treat as a signal, not a conclusion.
MLREF is a novel reinforcement learning framework designed to improve reward function design by maintaining and evolving a module pool of reusable reward components. Reported results show 25.2% improvement in locomotion and 6.6% in manipulation tasks relative to baselines, but the work is unpublished, simulation-based, and lacks independent validation or statistical significance testing.
Preprint. Intervention: Module Level Reward Evolution Framework (MLREF): a framework maintaining a persistent module pool of reusable reward components, evolved via reflection-based refinement, hybrid credit assignment, and merge-with-rollback strategy.. Compared with: Strong baselines (names not specified in abstract).
MLREF outperforms strong baselines by 25.2% in locomotion tasks MLREF outperforms strong baselines by 6.6% in manipulation tasks More stable optimization dynamics reported across iterations
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A single-centre computational study of a novel algorithmic framework on simulated tasks, reporting relative performance gains without peer review or clinical validation.
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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.
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