VLM Post-Training

VLM: Vision-Language Model

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13 papers3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

Subliminal Learning in Vision-Language Models
Nov 4, 2026Arman Hatami, Romina Aalishah, Ilya E. MonosovLarge Vision-Language Models VLM Post-Training
Johns Hopkins University, Baltimore, MD, USA
Rescuing Your Pruning: Rethinking Visual Token Pruning and Post-Training Recovery in MLLMs
Oct 24, 2026Peiran Yin, Hao Wu, Zihao Yang +3Visual Token Pruning Efficient VLM Inference
EIT-NLP Lab, Eastern Institute of Technology, Ningbo · Shanghai Jiao Tong University · Uppsala University
Do As I Do, Not As I Say: On-Policy Distillation Inherits the Teacher in Vision-Language Models
Oct 14, 2026Frank F. Yang, Kefei Yan, Yiren Wang +1VLM Distillation On-Policy Distillation
Georgia Institute of Technology, Atlanta, GA, USA
Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder
Oct 8, 2026Zidan Wang, Yaqian Li, Xiaokai Zhang +3VLM Post-Training VLM Adaptation
Huazhong University of Science and Technology · Li Auto Inc.
From Reward Signal to Visual Utility: A Controlled Audit of Medical VLM Post-Training
Sep 25, 2026Wang JingxinVLM Post-Training Vision-Language Models
Institute of Neuroscience, CAS
VisionPsy-Nano: Improving Accuracy, Efficiency, and Reliability in On-Device Vision-Language Models
Sep 23, 2026Khurram Azeem Hashmi, Mohammadreza Zolfaghari, Changdae Park +6VLM Post-Training Vision-Language Models
Tether AI Research
Post-Training VLMs for Video Mistake Detection
Aug 28, 2026Federico Spurio, Olga Zatsarynna, Lars Doorenbos +3VLM Post-Training Vision-Language Models
University of Bonn Bonn, Germany · Lamarr Institute for Machine Learning and Artificial Intelligence Bonn, Germany · Toyota Motor Europe Belgium
From Recovery to Drop-off: How Action Post-training Reduces a VLM's Late-Layer Depth Decodability
Aug 9, 2026Alexander Hackett, Arnaud Denis-Remillard, Axel CassouVLM Interpretability VLA Model Post-Training
New York University · Reflex · Université de Montréal +1
Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation
Jul 25, 2026Shuai Wang, Daoan Zhang, Zhe Tang +2VLM Adaptation On-Policy Self-Distillation
The Hong Kong University of Science and Technology (Guangzhou) · University of Rochester · Zhejiang University of Technology +1
From Seeing to Thinking: Decoupling Perception and Reasoning Improves Post-Training of Vision-Language Models
May 19, 2026Juncheng Wu, Hardy Chen, Haoqin Tu +6Vision-Language Models VLM Reasoning
Amazon · UC Santa Cruz · University of Waterloo +1
Watch Before You Answer: Learning from Visually Grounded Post-Training
Apr 6, 2026Yuxuan Zhang, EunJeong Hwang, Huaisong Zhang +8VLM Post-Training Vision-Language Models
University of British Columbia · Vector Institute · Etude AI +4
VLM-AutoDrive: Post-Training Vision-Language Models for Safety-Critical Autonomous Driving Events
Mar 18, 2026Mohammad Qazim Bhat, Yufan Huang, Niket Agarwal +7VLM Post-Training VLMs for Autonomous Driving
NVIDIA, California, USA
Learning Transferable Temporal Primitives for Video Reasoning via Synthetic Videos
Mar 18, 2026Songtao Jiang, Sibo Song, Chenyi Zhou +12VLM Post-Training Temporal Video Understanding
Zhejiang University · Qwen Team, Alibaba Group · Shanghai Jiao Tong University +1

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Subliminal Learning in Vision-Language Models

Arman Hatami, Romina Aalishah, Ilya E. Monosov

Johns Hopkins University, Baltimore, MD, USA

Large Vision-Language Models VLM Post-Training

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Subliminal learning is a phenomenon in large language models (LLMs) where a teacher passes a hidden preference to a student through training data unrelated to that preference. We study whether subliminal learning also occurs in vision-language models (VLMs). We finetune a teacher VLM to prefer a specific animal or facial attribute and use a control teacher from the same base model. Both teachers generate numbers for an unrelated visual task involving geometric shapes. Two students initialized from the same base VLM are finetuned on the same images, using numbers generated by either the biased or control teacher. Across VLMs ranging from 2B to 7B parameters, we find that both animal and facial preferences can transfer through this unrelated task. Students trained on biased-teacher outputs select the target more often than control students, with a difference of up to

\sim

10 percentage points in the animal preference task. Transfer can become stronger with more training data and varies with the target preference, model, and how the numbers are generated. Beyond preferences, we show that the biased student's explanations use more of the same words and phrases as the biased teacher's explanations than those of the control student, even when all three models select the same image. These results matter for training VLMs and show that training on unrelated data may change both a VLM's image preferences and how it explains its choices, and that transfer can become stronger with more training samples.