arXiv AI
DualOPSD: Closing the Feedback Loop in On-Policy Self-Distillation
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arXiv Machine Learning
Aug 27
DualOPSD: Adaptive Privileged Teachers for On-Policy Self-Distillation
DualOPSD introduces an asymmetric alternating framework for on-policy self-distillation, where a privileged teacher first trains the student and then adapts to the student’s updated distribution on the same trajectory, eliminating the need for additional rollouts. This adaptive approach improves average performance on AIME 2024, AIME 2025, and HMMT 2025 benchmarks for the Qwen3-8B model, with gains that vary by model scale. Across 1.7B, 4B, and 8B scales, DualOPSD also reduces truncation effects and lowers KL divergence between teacher and student.
arXiv AI
Aug 11
Privileged Solutions or Context-Induced Teacher Behavior? Dissecting On-Policy Self-Distillation
arXiv:2608. 09228v1 Announce Type: cross Abstract: On-Policy Self-Distillation (OPSD) is commonly interpreted as the transfer of privileged information: a teacher observes the verified solution to the target problem and supervises the student's trajectory.
arXiv Machine Learning
Sep 30
Overcoming Scaling Limits in On-Policy Self-Distillation for LLM Reasoning
arXiv:2609.37915v1 Announce Type: new Abstract: On-policy self-distillation (OPSD) trains a student to match a privileged teacher distribution along its own sampled trajectory. Standard OPSD applies...
arXiv AI
Aug 14
DAPD: Dual-Anchored Policy Distillation
arXiv:2608. 01735v2 Announce Type: replace Abstract: On-policy (self) distillation (OPSD) is increasingly adopted for language-model post-training.
arXiv AI
Sep 30
Interactive-Policy Distillation with Bidirectional Propose-and-Verify
arXiv:2609.36546v1 Announce Type: cross Abstract: On-policy distillation (OPD) trains a student model on its self-generated trajectories with dense token-level teacher feedback. However, naive OPD ma...
arXiv AI
Aug 11
WDL-OPD: Weak-Driven On-Policy Distillation via Mixture-Constrained Co-Training
arXiv:2608. 09447v1 Announce Type: cross Abstract: On-policy distillation (OPD) aligns a student with a teacher on trajectories sampled from the student itself, reducing the train-test state mismatch of offline distillation.