AI RESEARCH
GateKD: Confidence-Gated Closed-Loop Distillation for Robust Reasoning
arXiv CS.CL
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ArXi:2605.13136v1 Announce Type: new Distilling multi-step reasoning abilities from large language models (LLMs) into compact student models remains challenging due to noisy rationales, hallucinated supervision, and static teacher-student interactions. Existing reasoning distillation methods, including mentor-based approaches, predominantly operate in an open-loop manner, implicitly assuming uniform teacher reliability and consequently propagating erroneous intermediate reasoning.