arXiv AI July 7, 2026 By Aleksandar Todorov, Matthia Sabatelli

Learning in Low-Dimensional Subspaces: Orthogonal Bottlenecks for Reinforcement Learning

arXiv:2605. 26012v2 Announce Type: replace-cross Abstract: Deep reinforcement learning (RL) agents commonly rely on high-dimensional neural representations, despite growing evidence that task-relevant value and policy structure may be intrinsically low-dimensional.

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arXiv Machine Learning

Aug 27

Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models

The paper introduces Regularized Latent Dynamics Prediction (RLDP), a method that adds orthogonality regularization to self‑supervised next‑state prediction in latent space. RLDP maintains feature diversity, matching or surpassing complex representation learning approaches for zero‑shot reinforcement learning. It also performs robustly in low‑coverage data settings where prior methods fail.

By Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal, Amy Zhang, Scott Niekum, Martha White

agents diffusion reinforcement-learning benchmarks

arXiv Machine Learning

Jun 2

Task-Induced Representational Invariances Depend on Learning Objective in Deep RL

arXiv:2606. 01868v1 Announce Type: new Abstract: Reinforcement Learning (RL) has long served as a model for goal-directed animal behavior in neuroscience.

By Manu Srinath Halvagal, Sebastian Lee, SueYeon Chung

llms reinforcement-learning fine-tuning

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 3

GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR

arXiv:2601. 09361v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a key paradigm for improving large-scale reasoning models.

By Jiaying Zhang, Lei Shi, Jiguo Li, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He

llms reinforcement-learning fine-tuning

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

Sep 23

RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking

arXiv:2605.11151v3 Announce Type: replace Abstract: Offline-to-online reinforcement learning (RL) improves sample efficiency by leveraging pre-collected datasets prior to online interaction. A key ch...

By Andrew Choi, Wei Xu

reinforcement-learning robotics fine-tuning multimodal benchmarks

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Related stories

Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models

Task-Induced Representational Invariances Depend on Learning Objective in Deep RL

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

GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR

Representation Learning Enables Scalable Multitask Deep Reinforcement Learning

RankQ: Offline-to-Online Reinforcement Learning via Self-Supervised Action Ranking