AI RESEARCH
Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
arXiv CS.AI
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ArXi:2605.02290v1 Announce Type: new Distilling large reasoning models is essential for making Long-CoT reasoning practical, as full-scale inference remains computationally prohibitive. Existing curation-based approaches select complete reasoning traces post-hoc, overlooking collaboration among heterogeneous teachers and lacking dynamic exploration, which leads to redundant sampling and missed complementary reasoning. We