Long chain-of-thought reasoning improves vision-language models (VLMs), but it also exposes a temporal grounding failure: as generation unfolds, models may drift from image evidence while reinforcement learning still provides only a final-answer reward. We study this gap through visual revisit, a temporally localized r...
Zhaolu Kang, Tai-Long Luo, Chen-Xin Li et al.· 0 citations
Video multimodal large language models (MLLMs) keep climbing video question answering benchmarks, yet shuffling the frames, masking the segment that supports the answer, or occluding the target object barely changes their predictions. The accuracy rests on appearance and language priors, not on the temporal evidence th...
Zhaolu Kang, Shi-Yu Liu, Tai-Long Luo et al.· 0 citations
Savor is introduced, a training framework that augments the output schema with token and answer confidence, optimises the policy with a Group Relative Policy Optimisation objective that penalises calibration error and poor abstention decisions, and uses the learned confidence at inference time to revisit visual evidenc...
Zian Ding, Zi-Lin Zhao, Ying-Jie He et al.· arXiv.org· 0 citations
Execution feedback is a useful supervision signal for code models because unit tests are objective and directly measure program correctness. Its weakness is that an entire program is often reduced to one pass or fail bit, leaving RLVR to solve a difficult credit assignment problem. At the same time, coding systems ofte...
Jun Cao, Ying-Jie He· 0 citations
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