AI RESEARCH
STRIDE: Learnable Stepwise Language Feedback for LLM Reasoning
arXiv CS.LG
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ArXi:2605.18851v1 Announce Type: new Recent advances in Reinforcement Learning (RL) have underscored its potential for incentivizing reasoning capabilities of Large Language Models (LLMs). However, existing step-level efforts suffer from costly annotations that limit domain coverage, while scalar scores further impose an information bottleneck, offering insufficient semantic bandwidth to improve intermediate decisions.