arXiv AI June 30, 2026 By Hyunwoo Park, Sang-Hyun Lee

Domain Adaptation with Adaptive Imagination for Visual Reinforcement Learning under Limited Target Data

arXiv:2606. 30192v1 Announce Type: new Abstract: Sim-to-real transfer remains a major obstacle for reinforcement learning (RL), especially for vision-based control where image observations exacerbate the state-distribution shift between simulation and the real world.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning

Sep 22

Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds

arXiv:2603.18532v3 Announce Type: replace-cross Abstract: The strong performance of large vision-language models (VLMs) trained with reinforcement learning (RL) has motivated similar approaches for f...

By Andrew Choi, Xinjie Wang, Zhizhong Su, Wei Xu

llms reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 16

Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time

arXiv:2606. 15631v1 Announce Type: cross Abstract: Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute.

By Jeongeun Park, Juhan Park, Taekyung Kim, Sungjoon Choi, Dongyoon Han, Sangdoo Yun

rag robotics fine-tuning multimodal

arXiv Machine Learning

Jul 3

BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer

arXiv:2607. 01410v1 Announce Type: cross Abstract: Sim2real transfer for robot policy learning suffers due to mismatch between simulation and reality.

By Yunfu Deng, Josiah P. Hanna

robotics fine-tuning

arXiv Machine Learning

Jul 31

S-GRPO: Unified Post-Training for Large Vision-Language Models

arXiv:2604. 16557v2 Announce Type: replace Abstract: Current post-training methodologies for adapting Large Vision-Language Models (LVLMs) generally fall into two paradigms: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).

By Yuming Yan, Kai Tang, Sihong Chen, Ke Xu, Dan Hu, Qun Yu, Pengfei Hu

llms reinforcement-learning fine-tuning multimodal

arXiv AI

Jun 2

StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement

arXiv:2606. 00267v1 Announce Type: cross Abstract: Video world models (WMs) have shown promise for policy evaluation and improvement by imagining realistic future observations conditioned on ego-robot actions.

By Junwon Seo, Sushant Veer, Ran Tian, Wenhao Ding, Apoorva Sharma, Karen Leung, Edward Schmerling, Marco Pavone, Andrea Bajcsy

llms agents diffusion robotics multimodal benchmarks

arXiv AI

Jun 9

PRISM: PRior-guided Imagination Sampling in world Models

arXiv:2606. 07974v1 Announce Type: cross Abstract: A learned world model provides a powerful physical intuition for evaluating future states.

By Yuhai Wang, Jiawei Xia, Rongxuan Zhou, Xiao Hu, Yongliang Shi, Jing Du, Yang Ye

agents

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Related stories

Scaling Sim-to-Real VLA Reinforcement Learning with Generative 3D Worlds

Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time

BIFROST: Bridging Invariant Feature Representation for Observation-space Sim2Real Transfer

S-GRPO: Unified Post-Training for Large Vision-Language Models

StressDream: Steering Video World Models for Robust Policy Evaluation and Improvement

PRISM: PRior-guided Imagination Sampling in world Models