arXiv Machine Learning July 8, 2026 By Noah Farr, Aryaman Reddi, Carlo D'Eramo, Jan Peters

Streaming Reinforcement Learning under Partial Observability with Real-Time Recurrent Learning

Read the original on arXiv Machine Learning →

arXiv:2605. 24709v2 Announce Type: replace Abstract: Streaming reinforcement learning has emerged as an online learning paradigm that conforms to the restrictions of natural learning agents that process data incrementally, i.

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

arXiv AI

Jul 9

Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization

arXiv:2604. 01577v3 Announce Type: replace-cross Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory.

By Shota Takashiro, Masanori Koyama, Takeru Miyato, Yusuke Iwasawa, Yutaka Matsuo, Kohei Hayashi

llms reinforcement-learning safety

arXiv AI

Jun 9

MinMax Recurrent Neural Cascades

arXiv:2605. 06384v3 Announce Type: replace-cross Abstract: We introduce MinMax Recurrent Neural Cascades (MinMax RNCs), a class of recurrent neural networks built from a novel form of recurrence over the MinMax algebra.

By Alessandro Ronca

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

arXiv AI

Jun 8

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

arXiv:2601. 18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment.

By Yibo Li, Zijie Lin, Ailin Deng, Xuan Zhang, Yufei He, Shuo Ji, Tri Cao, Bryan Hooi

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 11

The Cell Must Go On: Agar.io for Continual Reinforcement Learning

arXiv:2505. 18347v3 Announce Type: replace-cross Abstract: Continual reinforcement learning (RL) concerns agents that are expected to learn continually, rather than converge to a policy that is then fixed for evaluation.

By Mohamed A. Mohamed, Kateryna Nekhomiazh, Vedant Vyas, Marcos M. Jose, Andrew Patterson, Marlos C. Machado

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 25

Frequency Domain Reservoir Computing

arXiv:2606. 24969v1 Announce Type: new Abstract: While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balance efficient training with highly expressive latent states.

By Klaus Schertler, Xiomara Runge, Andrea Ceni, David Kappel, Claudio Gallicchio

llms rag benchmarks

Related stories

Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization

MinMax Recurrent Neural Cascades

On the Role of Computation in Reinforcement Learning

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

The Cell Must Go On: Agar.io for Continual Reinforcement Learning

Frequency Domain Reservoir Computing