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cs.LG2026
DTDD: Divergence-Triggered Dynamic Distillation for Reliable On-Policy Supervision
Qijia He, Zequn Li, Spencer Luo +6
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 bec…
cs.LG2020★ 6 cited
Leveraging Organizational Resources to Adapt Models to New Data Modalities
Sahaana Suri, Raghuveer Chanda, Neslihan Bulut +7
As applications in large organizations evolve, the machine learning (ML) models that power them must adapt the same predictive tasks to newly arising data modalities (e.g., a new v…