SAKI: Maximal-Coupling-Routed Teacher Supervision for On-Policy Distillation
On-policy distillation (OPD) reduces train-test state mismatch by training a student on its own generated trajectories, but weak students may visit teacher-misaligned prefixes where supervision is less representative. We introduce SAKI (Supervision Allocation with KL-constrained Interpolation), which combines a KL-constrained teacher-guided rollout with maximal coupling and reuses realized accept/correction events to route token-level supervision. Accepted positions retain sampled-token reverse-KL supervision, while correction positions receive direct supervision on the teacher's highest-probability token. Under maximal coupling, the correction probability is exactly TV(p_t, q_t), so the ...
| Authors | Miteto Wei, Xiaohan Wang, Zehao Chen et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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