aiqbench analysis
This research introduces ReLAR, a reinforcement-guided framework that stabilizes large language model reasoning by iteratively refining hidden states before decoding without generating explicit chain-of-thought text. By adaptively controlling the number and direction of refinement steps via policy gradients, the method achieves higher accuracy on complex tasks like medical diagnosis and math while reducing inference overhead compared to traditional baselines. The approach offers a promising path toward more reliable LLMs in high-stakes multi-step scenarios where internal error propagation is critical.
Learning to Refine Hidden States for Reliable LLM Reasoning
arXiv:2606.17524v1 Announce Type: new 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.
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:2606.17524v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.17524 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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