Recovering General Capabilities via Uncertainty-Calibrated Multi-Teacher On-Policy Distillation
Specializing large language models to vertical domains improves domain-specific behavior but often degrades general capabilities. We study this trade-off in Multi-Teacher On-Policy Distillation (MOPD), where a specialized model learns from domain and general teachers on its own sampled trajectories. Standard MOPD faces two limitations: ordinary on-policy sampling rarely exposes tokens with large positive teacher--student advantages, and advantage sign alone does not establish whether the proposed update direction is reliable. We propose Uncertainty-Calibrated MOPD (UCMOPD), which addresses these limitations through two complementary mechanisms. Golden-Gain Enhancement combines higher-temp...
| Authors | Ziyuan Liu, Jiao Ou, Jian Liang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 27 August 2026 |
| Last updated at source | 28 September 2026 |
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