AI RESEARCH
CRAFT: Calibrated Reasoning with Answer-Faithful Traces via Reinforcement Learning for Multi-Hop Question Answering
arXiv CS.LG
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ArXi:2602.01348v2 Announce Type: replace-cross Retrieval-augmented large language models, when optimized with outcome-level rewards, can achieve strong answer accuracy on multi-hop questions. However, under noisy retrieval, models frequently suffer from "right-answer-wrong-reason failures": they may exploit spurious shortcuts or produce reasoning traces weakly grounded in the ing evidence. Furthermore, the lack of structured output control prevents reliable auditing of the underlying reasoning quality.