arXiv AI June 4, 2026 By Jashaswimalya Acharjee, Balaraman Ravindran

Unifying Model-Free Efficiency and Model-Based Representations via Latent Dynamics

arXiv:2602. 12643v2 Announce Type: replace-cross Abstract: We present Unified Latent Dynamics (ULD), a novel reinforcement learning algorithm that unifies the efficiency of model-free methods with the representational strengths of model-based approaches, without incurring planning overhead.

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

arXiv Machine Learning

Jun 5

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

arXiv:2606. 05555v1 Announce Type: new Abstract: Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge.

By Johan Obando-Ceron, Lu Li, Scott Fujimoto, Pierre-Luc Bacon, Aaron Courville, Pablo Samuel Castro

reinforcement-learning

Hugging Face Trending Papers

Jun 4

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Scaling reinforcement learning (RL) to diverse multitask settings remains a central challenge. While recent advances in model-based RL achieve strong performance, they rely on planning and complex training pipelines, making it unclear which components are essential for scalability.

reinforcement-learning

arXiv Machine Learning

Jul 9

Latent Policy Steering through One-Step Flow Policies

arXiv:2603. 05296v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration.

By Hokyun Im, Andrey Kolobov, Jianlong Fu, Youngwoon Lee

reinforcement-learning robotics efficiency benchmarks

arXiv AI

Jun 3

ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL

arXiv:2606. 03017v1 Announce Type: cross Abstract: Reward transfer in Inverse Reinforcement Learning (IRL) is unreliable when policies must generalize to unseen combinations of environment dynamics and task goals.

By Yikang Gui, Bikramjit Banerjee, Prashant Doshi

reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 18

Hierarchical Planning with Latent World Models

arXiv:2604. 03208v2 Announce Type: replace Abstract: World models are a promising path to zero-shot embodied control through planning.

By Wancong Zhang, Basile Terver, Artem Zholus, Soham Chitnis, Harsh Sutaria, Mido Assran, Randall Balestriero, Amir Bar, Adrien Bardes, Yann LeCun, Nicolas Ballas

diffusion reinforcement-learning robotics

arXiv AI

Aug 10

Flowing Through States: Neural ODE Regularization for Reinforcement Learning

arXiv:2608. 06595v1 Announce Type: cross Abstract: Neural networks applied to sequential decision-making tasks typically rely on latent representations of environment states.

By Mohamed Ghanem, Bernd Finkbeiner

rag agents reinforcement-learning benchmarks

Related stories

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

Latent Policy Steering through One-Step Flow Policies

ConTraIRL: Factorized Contrastive Abstractions for Transferable IRL

Hierarchical Planning with Latent World Models

Flowing Through States: Neural ODE Regularization for Reinforcement Learning