Learning to Refine Hidden States for Reliable LLM Reasoning

Abstract:Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings, where early hidden-state errors may propagate to incorrect predictions. We propose ReLAR, a reinforcement-guided latent refinement framework that iteratively updates hidden representations before decoding. ReLAR maintains a compact latent reasoning state and uses learned depth and action controllers to adaptively determine both the number and direction of refinement steps. The controllers are trained with a policy gradient objective based on step-wise likelihood improvement, enabling efficient input-dependent reasoning without explicit chain-of-thought generation. Experiments on medical, mathematical, multi-hop reasoning, and open-ended generation benchmarks show that ReLAR improves accuracy, generation quality, and reasoning stability with substantially lower inference overhead than explicit reasoning baselines.

Machine Learning (cs.LG)

arXiv:2606.17524 [cs.LG]

(or arXiv:2606.17524v3 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2606.17524 Focus to learn more arXiv-issued DOI via DataCite

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