GateKD: Confidence-Gated Closed-Loop Distillation for Robust Reasoning
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. We propose GateKD, a confidence-gated closed-loop distillation framework that enables robust reasoning transfer by treating the teacher as a dynamic gatekeeper rather than a static oracle. GateKD introduces three complementary mechanisms: (i) confi...
| Authors | Kasidit Sermsri, Teerapong Panboonyuen |
|---|---|
| Primary category | cs.CL |
| Published | 13 May 2026 |
| Last updated at source | 1 June 2026 |
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