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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.
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