CADENCE: Closing the Reasoning Gap via Coverage-Adaptive On-Policy Distillation

On-policy knowledge distillation transfers reasoning from large teachers to compact students, but existing approaches suffer three compounding failure modes: (i) cold-start collapse, where a fresh student assigns near-zero mass to teacher-preferred tokens; (ii) state-agnostic divergence scheduling, where time-only forward/reverse-KL interpolation ignores the student's coverage state; and (iii) binary reward sparsity, where pass/fail signals discard information from partially correct traces. We p

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ethics.ai (17 July 2026), “CADENCE: Closing the Reasoning Gap via Coverage-Adaptive On-Policy Distillation,” evidence record 14904, https://ethics.ai/record/14904 (originally published by HuggingFace Daily Papers).

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