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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

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

arXiv Machine Learning

Jun 19

DADP: Domain Adaptive Diffusion Policy

arXiv:2602. 04037v3 Announce Type: replace Abstract: Learning domain adaptive policies that can generalize to unseen transition dynamics, remains a fundamental challenge in learning-based control.

By Pengcheng Wang, Qinghang Liu, Haotian Lin, Yiheng Li, Guojian Zhan, Masayoshi Tomizuka, Yixiao Wang

diffusion robotics benchmarks

Related stories

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

DADP: Domain Adaptive Diffusion Policy