On-Policy Distillation for Vision-Language Model Adaptation, an Effective Paradigm on Low-Quality Multimodal Data
Paper recorded by Signals 4 on 2026-09-09 in cs.CL. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-09 on arXiv · recorded by Signals 4 on 2026-09-10
Category: cs.CL · 自然语言处理 · first seen 2026-09-10
Abstract
Knowledge distillation offers an efficient route to transfer a task-adapted vision-language teacher to a compact student. The training target in current vision-language distillation methods is typically constructed from the teacher prediction and applied uniformly to all training samples, making it unreliable under class and domain shifts. In this paper, we argue that distillation target construct
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Cite this page: On-Policy Distillation for Vision-Language Model Adaptation, an Effective Paradigm on Low-Quality Multimodal Data: the #83 most recent of 186 cs.CL papers we have recorded (as of 2026-09-09). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/on-policy-distillation-for-vision-language-model-adaptation-an-effective-paradig.html
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