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 present CADENCE, a unified framework with a targeted fix for each. Its DRIFT mechanism schedules a per-token convex mixture of forward-KL and reverse-KL surrogate objectives on student-sampled traje...
| Authors | Satyam Kumar, Saurabh Jha |
|---|---|
| Primary category | cs.LG |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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