Open multimodal reasoning models have benefited from large-scale reasoning supervision, yet reliable post-training remains challenging due to uneven data quality, inefficient supervision construction, imbalanced difficulty, and cross-domain interference. We introduce MMVistaReason (MVR), an open-data post-training reci...
Jue-Kai Lin, Hong-Lin Lin, Yu-Qian Yuan et al.· 0 citations
Outcome-Guided On-Policy Distillation is proposed, which applies trajectory-relative weighting to teacher supervision and calibrates these weights using final task outcomes from paired student continuations and selectively strengthens supervision on the student's original trajectories at turns where teacher guidance be...
Tong Zhang, Zhou Liu, Yi-Hao Liu et al.· 0 citations
An RL method tailored for context management is proposed, which uses context and entropy variation to identify critical editing decisions for branch sampling and estimates action-level advantages from all branched trajectories that pass through the corresponding context editing action.
Zhuo-Shi Pan, Qizhi Pei, Jun-Ru Lu et al.· 1 citation
A novel Dual Self-Consistency Reinforcement Learning optimization paradigm is introduced, which utilizes Round-Trip Verification to penalize degenerate code and boost overall self-consistency in TikZ code.
Juekai Lin, Yun Zhu, Honglin Lin et al.· arXiv.org· 5 citations
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