作者位次 #2/5ACL 2026Findings· Findings of ACL
R i T : Rubrics-in-Thinking Reinforcement Learning for Improved Reasoning in Large Language Models
Xiaobin Tian · Shuai Yuan · Muyun Ding · Haonan Chen · xiaoxi jiang
摘要
Large Reasoning Models (LRMs) benefit from generating intermediate reasoning steps, enabling more reliable and interpretable decision-making. While outcome-based supervision has proven effective for LRMs across diverse tasks, it focuses solely on final answers and cannot guarantee high-quality in...