VC-OPD: Visual Counterfactual On-Policy Distillation for Grounded Vision-Languag
arXiv:2603.18532v1 Announce Type: cross Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar
arXiv:2605.11458v1 Announce Type: new Abstract: On-policy self-distillation has become a strong recipe for LLM reasoning, where a privileged teacher supervises the student's own
arXiv:2610.02678v1 Announce Type: new Abstract: On-policy distillation (OPD) combines student-generated rollouts with dense token-level supervision from a teacher, but providing
arXiv:2605.11556v1 Announce Type: new Abstract: Solving complex long-horizon tasks requires strong planning and reasoning capabilities. Although datasets with explicit
arXiv:2605.01327v1 Announce Type: new Abstract: Existing reinforcement learning approaches for Large Language Models typically perform policy optimization at the granularity of
arXiv:2610.21195v1 Announce Type: new Abstract: On-policy distillation (OPD) improves a student language model by training on its own generated trajectories with supervision from
arXiv:2605.00642v1 Announce Type: new Abstract: Graphical User Interface (GUI) grounding maps natural language instructions to the visual coordinates of target elements and serves
arXiv:2609.21619v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning models by learning the token-level discrepancy between a stronger teacher and an
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