arXiv AI
What Shared Prefixes Hide: Trajectory Dropout for On-Policy Distillation
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Trajectory-Refined Distillation
arXiv:2606. 08432v1 Announce Type: new Abstract: On-policy distillation (OPD) has become a central post-training tool for large language models (LLMs), providing dense per-token teacher supervision along the student's own rollouts.
arXiv AI
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Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance
arXiv:2606. 00305v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) improves large language model reasoning by training a student model on trajectories sampled from its own policy under teacher supervision.
arXiv Machine Learning
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Demystifying On-Policy Distillation: Roles, Pathologies, and Regulations
arXiv:2607. 13399v1 Announce Type: cross Abstract: On-policy distillation (OPD) has become a key paradigm in LLM post-training, yet its training dynamics remain poorly understood.
arXiv AI
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When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation
arXiv:2608. 03632v1 Announce Type: new Abstract: On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals.
arXiv Machine Learning
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Prefix-Guided On-Policy Distillation: Mining Golden Trajectories from Rollouts
arXiv:2606. 21994v2 Announce Type: replace Abstract: On-policy distillation (OPD) improves reasoning models by applying dense teacher supervision on student-sampled trajectories.
Hugging Face Trending Papers
Aug 4
When Teachers Mislead: Spurious-Signal-Aware On-Policy Distillation
On-Policy distillation (OPD) transfers teacher capabilities by supervising student-sampled trajectories with dense token-level teacher signals. Recent selective OPD methods improve this process by prioritizing signals that are confident, informative, or learnable.