arXiv AI

Learning What to Predict: Downstream-Guided Task Design for Continued Pretraining

arXiv:2601. 22108v2 Announce Type: replace-cross Abstract: Continued pretraining is optimized with fixed self-supervised tasks but selected by downstream performance, creating a coarse feedback loop in which practitioners evaluate checkpoints, change data mixtures or objectives, and restart runs, while individual updates remain blind to target capabilities.

arXiv AI

Jun 2

VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models

arXiv:2601. 03309v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising generalization capabilities.

By Jianke Zhang, Xiaoyu Chen, Qiuyue Wang, Mingsheng Li, Yanjiang Guo, Yucheng Hu, Jiajun Zhang, Shuai Bai, Junyang Lin, Jianyu Chen

llms computer-vision robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 28

Self-Boosting Vision-Language Models with Noisy Student On-Policy Self-Distillation

arXiv:2607. 23125v1 Announce Type: new Abstract: Post-training enables vision-language models (VLMs) to understand human instructions and perform various downstream tasks.

By Shuai Wang, Daoan Zhang, Zhe Tang, Hao Cheng, Jiaheng Wei

llms reinforcement-learning efficiency multimodal benchmarks

arXiv Machine Learning

Aug 3

Visual Distribution Anchoring for Efficient Prompt Tuning

arXiv:2607. 28967v1 Announce Type: cross Abstract: Prompt tuning adapts vision--language models with few trainable parameters, but existing approaches trade off efficiency and adaptation: static textual prompts can overfit source classes, image-conditioned prompts add per-instance computation, and multimodal tuning modifies the visual branch.

By Pouya Parsa, Raoof Zare Moayedi, Seongjin Choi

llms multimodal

arXiv Machine Learning

Jul 28

Self-Distillation of Hidden Layers for Self-Supervised Representation Learning

arXiv:2603. 15553v2 Announce Type: replace-cross Abstract: The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.

By Scott C. Lowe, Anthony Fuller, Sageev Oore, Evan Shelhamer, Graham W. Taylor

rag computer-vision efficiency

arXiv AI

Jul 2

LeVLJEPA: End-to-End Vision-Language Pretraining Without Negatives

arXiv:2607. 00784v1 Announce Type: cross Abstract: Vision-language pretraining remains dominated by contrastive objectives, whereas vision-only self-supervised learning has largely adopted non-contrastive methods.

By Lukas Kuhn, Giuseppe Serra, Randall Balestriero, Florian Buettner

llms rag computer-vision multimodal

arXiv AI

Jun 3

See Less, Specify More: Visual Evidence Budgets for Generalizable VLAs

arXiv:2606. 02735v1 Announce Type: cross Abstract: Generalization remains a central bottleneck for vision-language-action (VLA) models: under distractors, appearance shifts, and semantically similar tasks, the policy must often infer local execution details from coarse instructions while also deciding which parts of the image matter for control.

By Yueh-Hua Wu, Tatsuya Matsushima, Kei Ota

llms robotics multimodal

arXiv AI

Jun 6

Image Generators are Generalist Vision Learners

arXiv:2604. 20329v3 Announce Type: replace-cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent capabilities of language understanding and reasoning from generative pretraining.

By Valentin Gabeur, Shangbang Long, Songyou Peng, Paul Voigtlaender, Shuyang Sun, Yanan Bao, Karen Truong, Zhicheng Wang, Wenlei Zhou, Jonathan T. Barron, Kyle Genova, Nithish Kannen, Sherry Ben, Yandong Li, Mandy Guo, Suhas Yogin, Yiming Gu, Huizhong Chen, Oliver Wang, Saining Xie, Howard Zhou, Kaiming He, Thomas Funkhouser, Jean-Baptiste Alayrac, Radu Soricut

llms diffusion computer-vision benchmarks

arXiv AI

Jun 18

From Memorization to Parameter Interference: How Overtraining Experts Harms Model Merging

arXiv:2506. 14126v2 Announce Type: replace-cross Abstract: Modern deep learning is increasingly characterized by the use of open-weight foundation models that can be fine-tuned on specialized datasets.

By Stefan Horoi, Guy Wolf, Eugene Belilovsky, Gintare Karolina Dziugaite

fine-tuning

arXiv AI

Jun 2

On the Limits of Token Reduction for Efficient Unified Vision Language Training

arXiv:2606. 01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint training is computationally expensive and largely overlooked from an efficiency perspective.

By Siyi Chen, Weiming Zhuang, Jingtao Li, Lingjuan Lv

llms multimodal

arXiv AI

Jun 12

Iterative Visual Thinking: Teaching Vision-Language Models Spatial Self-Correction through Visual Feedback

arXiv:2606. 13156v1 Announce Type: cross Abstract: Vision-language models (VLMs) achieve strong singleshot spatial grounding, yet lack any mechanism to observe and correct their own predictions.

By Animesh Tripathy, Aswanth Krishnan

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 9

Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training

arXiv:2605. 04913v4 Announce Type: replace-cross Abstract: LLM post-training typically propagates task gradients through the full depth of the model.

By Hengyu Shi, Tianyang Han, Peizhe Wang, Zhiling Wang, Xu Yang, Junhao Su

llms

arXiv Machine Learning

Jul 9

Efficient Long-Horizon Learning for Learned Optimization

arXiv:2607. 06772v1 Announce Type: new Abstract: Learned optimization aims to improve upon hand-designed optimizers (e.

By Xiaolong Huang, Benjamin Th\'erien, James Harrison, Eugene Belilovsky

llms computer-vision

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