arXiv AI June 19, 2026 By Darrien McKenzie, Nicklas Hansen, Xiaolong Wang

Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models

arXiv:2606. 19750v1 Announce Type: cross Abstract: Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems are sampled during optimization.

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arXiv Machine Learning

Jul 31

On-Policy and Off-Policy Learning for Large Action Spaces

arXiv:2607. 28408v1 Announce Type: new Abstract: This thesis studies policy learning in interactive systems where an agent observes a context, selects an action from a very large set, and receives partial feedback.

By Imad Aouali

agents diffusion safety

arXiv Machine Learning

Sep 23

RepUCB: Representation Learning-Based UCB for Heterogeneous Multi-Task Linear Bandits

arXiv:2604. 00531v2 Announce Type: replace Abstract: Multi-task representation learning exploits the shared structure among related tasks by learning a common latent representation, thereby improving sample efficiency.

By Jiabin Lin, Shana Moothedath

reinforcement-learning

arXiv AI

Aug 18

Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors

arXiv:2608. 16707v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as decision-making agents in settings that require sophisticated environmental exploration.

By David Eric Austin, Kaheer Suleman, Jackie Chi Kit Cheung

llms agents reinforcement-learning safety

arXiv AI

Aug 5

In-Context Pure Exploration in Continuous Decision Spaces

arXiv:2602. 17976v2 Announce Type: replace-cross Abstract: In active sequential testing, also termed pure exploration, a learner is tasked with the goal to adaptively acquire information so as to identify an unknown ground-truth hypothesis with as few queries as possible.

By Alessio Russo, Yin-Ching Lee, Ryan Welch, Aldo Pacchiano

reinforcement-learning

arXiv Machine Learning

Sep 1

Amortizing intractable inference in large language models

arXiv:2310.04363v3 Announce Type: replace Abstract: Autoregressive large language models (LLMs) compress knowledge from their training data through next-token conditional distributions. This limits t...

By Edward J. Hu, Moksh Jain, Eric Elmoznino, Younesse Kaddar, Guillaume Lajoie, Yoshua Bengio, Esmeralda S. Whitammer

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Aug 19

Latent Order Bandits

The paper introduces Latent Order Bandits (LOB), a new bandit framework that relaxes the strict assumptions of traditional latent bandits by only requiring a partial order of action preferences within each latent state. LOB allows instances sharing the same state to have different reward distributions as long as the action ranking remains consistent, making it suitable for scenarios like user groups on streaming services who agree on genre preferences but rate differently. The authors present an upper‑confidence bound algorithm for both total and partial latent orders, provide regret bounds, and propose a posterior‑sampling variant that empirically outperforms full‑prior latent bandits when reward scales vary across instances sharing the same latent state.

By Emil Carlsson, Newton Mwai, Fredrik D. Johansson

reinforcement-learning

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Related stories

On-Policy and Off-Policy Learning for Large Action Spaces

RepUCB: Representation Learning-Based UCB for Heterogeneous Multi-Task Linear Bandits

Semantic Bandits: In-Context Exploration-Exploitation is Biased by Semantic Priors

In-Context Pure Exploration in Continuous Decision Spaces

Amortizing intractable inference in large language models

Latent Order Bandits