Jui-Ming Yao

Learning to Refine Hidden States for Reliable LLM Reasoning

ReLAR is proposed, a reinforcement-guided latent refinement framework that iteratively updates hidden representations before decoding that improves accuracy, generation quality, and reasoning stability with substantially lower inference overhead than explicit reasoning baselines.