Hugging Face Trending Papers June 4, 2026

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

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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.

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reinforcement-learning

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

arXiv AI

Jun 4

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.

By Jashaswimalya Acharjee, Balaraman Ravindran

rag diffusion reinforcement-learning robotics

arXiv AI

Aug 7

Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination

arXiv:2605. 04568v3 Announce Type: replace-cross Abstract: State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy networks, or a combination of policy networks and planning.

By Jonathan Spieler, Sven Behnke

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 3

Laplacian Representations for Decision-Time Planning

arXiv:2602. 05031v2 Announce Type: replace Abstract: Planning with a learned model remains a key challenge in model-based reinforcement learning (RL).

By Dikshant Shehmar, Matthew Schlegel, Matthew E. Taylor, Marlos C. Machado

diffusion reinforcement-learning benchmarks

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 Machine Learning

Jun 16

On the Role of Computation in Reinforcement Learning

arXiv:2602. 05999v3 Announce Type: replace Abstract: How does the amount of compute available to a reinforcement learning (RL) policy affect its learning?

By Raj Ghugare, Micha{\l} Bortkiewicz, Alicja Ziarko, Benjamin Eysenbach

reinforcement-learning

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Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

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

Dream-MPC: Gradient-Based Model Predictive Control with Latent Imagination

Laplacian Representations for Decision-Time Planning

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On the Role of Computation in Reinforcement Learning