DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision
On-policy distillation (OPD) provides dense token-level supervision on student rollouts, but this supervision can become unreliable when the student and teacher diverge, either because of a large initial policy gap or compounding errors in long-horizon reasoning and tool use. We introduce Divergence-Triggered Dynamic D...
Qi-Jia He, Ze-Qun Li, Spencer Luo et al.
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