← Back to all news
arXiv AI September 30, 2026 By Minung Kim, Jeongmo Kim, Gwanwoo Choi, Seungyul Han

Generative Support Realignment for Cross-Domain Offline Reinforcement Learning

Read the original on arXiv AI →

The Flow has not summarised this story yet — read it at arXiv AI.

  • reinforcement-learning
  • benchmarks

One email a morning, machine-written

One email a day, machine-written, one click to leave. We never share your address.

Related stories

arXiv AI
Jul 17

RAD: Retrieval High-quality Demonstrations to Enhance Decision-making

arXiv:2507. 15356v2 Announce Type: replace Abstract: Offline reinforcement learning (RL) learns policies from fixed datasets, thereby avoiding costly or unsafe environment interactions.

By Lu Guo, Yixiang Shan, Zhengbang Zhu, Qifan Liang, Lichang Song, Ting Long, Weinan Zhang, Yi Chang
agentsreinforcement-learningbenchmarks
More like this →
arXiv AI
Jun 24

ASALT: Adaptive State Alignment for Lateral Transfer in Multi-agent Reinforcement Learning

arXiv:2606. 24601v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) addresses the problem of training multiple agents that pursue collaborative, competitive, or mixed objectives.

By Anurag Akula, Satheesh K. Perepu, Abhishek Sarkar, Kaushik Dey
ragagentsreinforcement-learningfine-tuningbenchmarkssafety
More like this →
arXiv AI
Jul 20

DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning

arXiv:2607. 16090v1 Announce Type: cross Abstract: Transferring policies across domains poses a vital challenge in reinforcement learning, due to the dynamics mismatch between the source and target domains.

By Hanyang Chen, Anirudh Satheesh, Longchao Da, Hua Wei
diffusionreinforcement-learningfine-tuning
More like this →
arXiv Machine Learning
Aug 24

Decoupling Policy Extraction for Offline Reinforcement Learning

arXiv:2608.20909v1 Announce Type: new Abstract: Offline RL methods commonly jointly train the actor and critic, where the critic is used to guide the actor toward higher-value actions. This coupled l...

By Xuyao Lin, Yixiang Shan, Jinru Duan, Tao Yang, Xinyu Zhao, Runyu Lei, Yiming Zhao, Jiaxin Fan, Zongbao Feng, Peng Jia
ragreinforcement-learning
More like this →
arXiv AI
Jul 7

The Three Regimes of Offline-to-Online Reinforcement Learning

arXiv:2510. 01460v4 Announce Type: replace-cross Abstract: Offline-to-online reinforcement learning (RL) has emerged as a practical paradigm that leverages offline datasets for pretraining and online interactions for fine-tuning.

By Lu Li, Tianwei Ni, Yihao Sun, Pierre-Luc Bacon
reinforcement-learningfine-tuning
More like this →
arXiv AI
Jul 3

Generalization in offline RL: The structure is more important than the amount of pessimism

arXiv:2607. 02288v1 Announce Type: cross Abstract: While pessimism counteracts overestimation bias in offline reinforcement learning (RL), being overly conservative has been associated with hindering certain forms of generalization.

By Max Weltevrede, Matthijs T. J. Spaan, Wendelin B\"ohmer
reinforcement-learningsafety
More like this →
About Pricing API Newsletter Sources Privacy Terms Refunds Accessibility Provider info Contact RSS

The Flow links to publishers and never republishes their articles. Summaries are machine-generated.

v1.1.0 · 5f852ea