arXiv AI June 10, 2026 By Thanh Nguyen, Tri Ton, Hongbin Choe, Tung M. Luu, Chang D. Yoo

Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning

arXiv:2606. 10613v1 Announce Type: cross Abstract: Diffusion-based Q-learning has emerged as a powerful paradigm for offline reinforcement learning, but its reliance on multi-step denoising makes both training and inference computationally expensive and brittle.

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

arXiv AI

Jun 16

QPILOTS: Efficient Test-Time Q-Steering for Flow Policies

arXiv:2606. 14801v1 Announce Type: cross Abstract: Flow-matching and diffusion policies are expressive action generators, but optimizing them with temporal-difference reinforcement learning (RL) remains difficult.

By Yifan Ruan, Chenyang Cao, Andreas Burger, Ali Pesaranghader, Kaveh Kamali, Jaehong Kim, Nandita Vijaykumar, Alan Aspuru-Guzik, Igor Gilitschenski, Nicholas Rhinehart

diffusion reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Jun 2

Drift Q-Learning

arXiv:2606. 00350v1 Announce Type: cross Abstract: Offline reinforcement learning requires improving a policy from fixed data while avoiding out-of-distribution actions with unreliable value estimates.

By Anas Houssaini, Mohamad H. Danesh, Amin Abyaneh, Scott Fujimoto, Hsiu-Chin Lin, David Meger

diffusion reinforcement-learning efficiency data-engineering

arXiv AI

Aug 3

RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving

arXiv:2602. 07339v2 Announce Type: replace Abstract: Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployment.

By Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy

agents diffusion reinforcement-learning robotics efficiency multimodal safety

arXiv AI

Jul 13

Shortcut Trajectory Planning for Efficient Offline Reinforcement Learning

arXiv:2607. 09336v1 Announce Type: cross Abstract: Diffusion-based trajectory planners have shown strong performance in offline reinforcement learning, but their iterative denoising process often incurs high inference cost.

By Guanquan Wang, Yoshimasa Tsuruoka

diffusion reinforcement-learning robotics efficiency benchmarks

arXiv Machine Learning

Aug 5

Revisiting TD Target Aggregation under Uncertainty in Q-Learning

arXiv:2608. 03069v1 Announce Type: new Abstract: Deep Q-Networks (DQNs) learn value functions through bootstrapped temporal-difference updates, where future returns are approximated using a greedy maximization over next-state action values.

By Lipeng Zu, Xiaonan Zhang

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 16

Temporal Difference Learning for Diffusion Models

arXiv:2606. 15048v1 Announce Type: new Abstract: Diffusion models are typically trained with objectives that focus on local denoising targets at individual time steps (or adjacent pairs), which do not enforce consistency between predictions along the denoising trajectory.

By Qizhen Ying, Yangchen Pan, Victor Adrian Prisacariu, Junfeng Wen

diffusion reinforcement-learning benchmarks

Related stories

QPILOTS: Efficient Test-Time Q-Steering for Flow Policies

Drift Q-Learning

RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving

Shortcut Trajectory Planning for Efficient Offline Reinforcement Learning

Revisiting TD Target Aggregation under Uncertainty in Q-Learning

Temporal Difference Learning for Diffusion Models