Right Answer, Wrong Tokens: Tail-Aware Credit Calibration for RLVR
arXiv:2607.07976v2 Announce Type: replace-cross Abstract: Although reinforcement learning (RL) improves the reasoning capabilities of large language models (LLMs), GRPO-style methods can suffer from a positive-credit contamination failure mode. Specifically, uniform credit assignment can reinforce locally erroneous tail tokens alongside useful tokens within successful trajectories. To address this problem, we propose Tail-Aware Credit calibratiOn (TACO), which calibrates the positive credit ass…