VLM Distillation

VLM: Vision-Language Model

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21 papers in the last four weeks, up 250% on the four weeks before. 0.2% of all new papers.

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Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding
May 30, 2026Hee Suk Yoon, Eunseop Yoon, Jaehyun Jang +6Cross-Modal Knowledge Distillation VLM Distillation
Korea Advanced Institute of Science and Technology (KAIST) · University of Illinois Urbana-Champaign (UIUC) · Microsoft Research Asia (MSRA)
Multimodal Distribution Matching for Vision-Language Dataset Distillation
May 22, 2026Jongoh Jeong, Hoyong Kwon, Minseok Kim +1Cross-Modal Learning Cross-Modal Alignment
Visual Intelligence Lab., KAIST
Visual-Advantage On-Policy Distillation for Vision-Language Models
May 21, 2026Ruiqi Liu, Xiaolei Lv, Gengsheng Li +8Vision-Language Models VLM Distillation
Institute of Automation, CAS · School of Advanced Interdisciplinary Sciences, UCAS · 3Hello +1
Distill to Think, Foresee to Act: Cognitive-Physical Reinforcement Learning for Autonomous Driving
May 20, 2026Yang Wu, Qiang Meng, Zhaojiang Liu +3End-to-End Autonomous Driving World Models
SJTU · FDU · NJU
Preferences Order, Ratings Anchor: From Fused Expert Aesthetic Ground Truth to Self-Distillation
May 19, 2026Yuanpei Zhao, Jie Lin, Chao Zhang +5Pairwise Preference Learning Personalized Image Aesthetics Assessment
1Sichuan University · 2NetEase Fuxi AI Lab · †Work done during an internship at NetEase Fuxi AI Lab. +1
HEED: Density-Weighted Residual Alignment for Hybrid Vision-Language Model Distillation
May 16, 2026Yihao Liang, Niraj K. JhaCross-Modal Knowledge Distillation Efficient VLM Inference
Princeton University
GeoWorld-VLM: Geometry from World Models for Vision-Language Models
May 15, 2026Renjie Gu, Kaichen Zhou, Yan Luo +1Visual Spatial Reasoning VLM Adaptation
Harvard AI and Robotics Lab · Kempner Institute for the Study of Natural and Artificial Intelligence · Harvard University
Offline Semantic Guidance for Efficient Vision-Language-Action Policy Distillation
May 15, 2026Jin Shi, Brady Zhang, Yishun LuVLM Distillation Efficient VLA Model Inference
Department of Mechanical Engineering University College London London, United Kingdom · Department of Engineering Science University of Oxford Oxford, United Kingdom
DeltaPrompts: Escaping the Zero-Delta Trap in Multimodal Distillation
May 15, 2026Jaehun Jung, Hyunwoo Kim, Brandon Cui +4VLM Distillation VLM Reasoning
NVIDIA Research
Hide to See: Reasoning-prefix Masking for Visual-anchored Thinking in VLM Distillation
May 12, 2026Seonghoon Yu, Dongjun Nam, Byung-Kwan Lee +1Cross-Modal Knowledge Distillation CoT Distillation
1KAIST · 3POSTECH · 2NVIDIA
OmniThoughtVis: A Scalable Distillation Pipeline for Deployable Multimodal Reasoning Models
May 12, 2026Yuanhao Yue, Chengyu Wang, Yuanjie Lyu +2VLM Distillation Multimodal Reasoning
Alibaba Group, Hangzhou, China
LLaVA-CKD: Bottom-Up Cascaded Knowledge Distillation for Vision-Language Models
May 11, 2026Nikolaos Gkalelis, Vasileios MezarisVision-Language Models Efficient VLM Inference
CERTH-ITI, Thessaloniki, Greece, 57001
Distilling 3D Spatial Reasoning into a Lightweight Vision-Language Model with CoT
May 10, 2026Alaa Asfour, Christopher Indris, Leihan Chen +2Cross-Modal Knowledge Distillation 3D Spatial Reasoning
Department of Computer Science, Toronto Metropolitan University, Toronto, ON, Canada
Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models
Apr 16, 2026Haoyi Sun, Xiaoxiao Wang, Ning Mao +5Cross-Modal Knowledge Distillation Vision-Language Models
Li Auto Inc.
BARD: Bridging AutoRegressive and Diffusion Vision-Language Models Via Highly Efficient Progressive Block Merging and Stage-Wise Distillation
Apr 15, 2026Baoyou Chen, Hanchen Xia, Peng Tu +5Vision-Language Models VLM Adaptation
Shanghai Academy of AI for Science · Fudan University · Shanghai Innovation Institute +1
Gated Relational Alignment via Confidence-based Distillation for Efficient VLMs
Jan 30, 2026Yanlong Chen, Amirhossein Habibian, Luca Benini +1Cross-Modal Knowledge Distillation Vision-Language Models
Department of Information Technology and Electrical Engineering, ETH Zurich, Zurich, Switzerland · School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore · Qualcomm AI Research, Amsterdam, the Netherlands +1
GenRecal: Generation after Recalibration from Large to Small Vision-Language Models
Jun 18, 2025Byung-Kwan Lee, Ryo Hachiuma, Yong Man Ro +2Cross-Modal Knowledge Distillation VLM Distillation
NVIDIA · KAIST
CLIP-RD: Relational Distillation for Efficient CLIP Knowledge Distillation
Date pendingJeannie Chung, Hanna Jang, Ingyeong Yang +2Cross-Modal Learning Cross-Modal Knowledge Distillation
Ewha Womans University, Seoul 03760, South Korea

May 30, 2026, cs.CVJ/K move · Enter open · S save

Decomposed On-Policy Distillation for Vision-Language Reasoning: Steering Gradients for Visual Grounding

Hee Suk Yoon, Eunseop Yoon, Jaehyun Jang, SooHwan Eom+5

Korea Advanced Institute of Science and Technology (KAIST) · University of Illinois Urbana-Champaign (UIUC) · Microsoft Research Asia (MSRA)

Cross-Modal Knowledge Distillation VLM Distillation Vision-Language Grounding VLM Reasoning On-Policy Distillation

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While on-policy distillation offers dense supervision for training small reasoning models, its optimization dynamics in the multimodal domain remain under-explored. In this work, we challenge the standard monolithic view of Vision-Language Model (VLM) distillation by mathematically decomposing the loss into two distinct components: the language prior and visual grounding. Our analysis uncovers that gradient vectors for these components are nearly orthogonal, indicating that the objective of aligning with the teacher's language distribution is geometrically independent from the objective of matching its visual perception. Consequently, standard optimization passively follows a suboptimal compromise trajectory that implicitly balances the two objectives. Hypothesizing that visual grounding constitutes the primary bottleneck for vision-language reasoning, we introduce Visual Gradient Steering (VGS), a method that dynamically reorients the update vector to prioritize the visual subspace. Experimental results on multiple distillation settings and complex multimodal benchmarks demonstrate that VGS significantly outperforms the standard monolithic formulation of on-policy distillation, achieving superior grounding with minimal training overhead.

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