arXiv Machine Learning June 2, 2026 By Wenhao Li, Xiangfeng Wang, Bo Jin

Mean-Field Diffuser: Scaling Offline MARL to Thousands of Agents

Read the original on arXiv Machine Learning →

arXiv:2605. 30190v2 Announce Type: replace Abstract: Diffusion-based planning has achieved strong results in single-agent offline reinforcement learning, yet scaling to many-agent systems remains intractable due to the curse of dimensionality in the joint trajectory space.

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

arXiv Machine Learning

Jun 19

Learning to Emulate Chaos: Adversarial Optimal Transport Regularization

arXiv:2604. 21097v2 Announce Type: replace-cross Abstract: Chaos arises in many complex dynamical systems, from weather to power grids, but is difficult to accurately model with data-driven methods such as machine learning emulators.

By Gabriel Melo, Leonardo Santiago, Peter Y. Lu

safety

arXiv AI

Jun 11

Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

arXiv:2602. 18291v2 Announce Type: replace Abstract: Online Multi-Agent Reinforcement Learning (MARL) is a prominent framework for efficient agent coordination.

By Zhuoran Li, Hai Zhong, Xun Wang, Qingxin Xia, Lihua Zhang, Longbo Huang

agents diffusion reinforcement-learning multimodal benchmarks

arXiv AI

Jun 29

RS-Diffuser: Risk-Sensitive Diffusion Planning with Distributional Value Guidance

arXiv:2606. 27766v1 Announce Type: cross Abstract: Offline reinforcement learning enables policy learning from fixed datasets without additional environment interaction, making it appealing for safety-critical applications where online exploration is costly or unsafe.

By Shiqiang Gong

diffusion reinforcement-learning robotics multimodal benchmarks safety

arXiv Machine Learning

Jun 9

Population-Aware Imitation Learning in Mean-field Games with Common Noise

arXiv:2605. 03357v2 Announce Type: replace Abstract: Mean Field Games (MFGs) provide a powerful framework for modeling the collective behavior of large populations of interacting agents.

By Gr\'egoire Lambrecht, Mathieu Lauri\`ere

agents safety

arXiv AI

Jun 6

Retry Policy Gradients in Continuous Action Spaces

arXiv:2606. 05888v1 Announce Type: new Abstract: Retry-based objectives such as pass@K and max@K optimize the best return obtained from multiple sampled trajectories, and recent work has shown that they can promote exploration without explicit exploration bonuses.

By Soichiro Nishimori, Paavo Parmas

arXiv AI

Jun 9

Reinforcement Learning for Flow-Matching Policies with Density Transport

arXiv:2606. 08602v1 Announce Type: cross Abstract: We present an online reinforcement learning (RL) algorithm for fine-tuning flow-matching policies in continuous-control problems.

By Boshu Lei, Kostas Daniilidis, Antonio Loquercio

diffusion reinforcement-learning robotics fine-tuning efficiency multimodal safety

Related stories

Learning to Emulate Chaos: Adversarial Optimal Transport Regularization

Diffusing to Coordinate: Efficient Online Multi-Agent Diffusion Policies

RS-Diffuser: Risk-Sensitive Diffusion Planning with Distributional Value Guidance

Population-Aware Imitation Learning in Mean-field Games with Common Noise

Retry Policy Gradients in Continuous Action Spaces

Reinforcement Learning for Flow-Matching Policies with Density Transport