arXiv AI July 7, 2026 By Faid Keddouri, Sohaib Houhou, Aissa Boulmerka, Nadir Farhi

Regime-Conditional Stabilisation of LLM-Augmented Cooperative Multi-Agent Reinforcement Learning

arXiv:2607. 04470v1 Announce Type: cross Abstract: Large Language Models (LLMs) offer a natural interface for translating human objectives into reward signals for cooperative multi-agent reinforcement learning (MARL), yet the training-time dynamics of this integration remain poorly understood.

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

arXiv Machine Learning

Jul 30

Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback

arXiv:2607. 26094v1 Announce Type: new Abstract: Reinforcement Learning from Human Feedback (RLHF) is the standard approach for aligning large language models with human preferences, but its quality is limited by static, task-agnostic reward models.

By Yunpeng Chu

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Jul 1

Predictable GRPO: A Closed-Form Model of Training Dynamics

arXiv:2606. 30789v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO) has become a standard tool for improving the reasoning ability of large language models, yet its training dynamics are still described empirically: reward trajectories are fit with low-parameter functional forms whose constants carry no mechanistic meaning, and hyperparameter choices remain a matter of trial and error.

By Rajat Ghosh, Datta Nimmaturi, Aryan Singhal, Vaishnavi Bhargava, Henry Wong, Johnu George, Debojyoti Dutta

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 18

Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards

arXiv:2606. 18963v1 Announce Type: new Abstract: We study online reward-punishment learning when the environment provides no scalar reward or evaluative label.

By Zirong Li

agents

arXiv Machine Learning

Aug 4

RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States

arXiv:2608. 02508v1 Announce Type: new Abstract: Learning-based memory systems for self-evolving LLM agents face two tightly coupled challenges.

By Yi Yang, Zhennan Chen, Yihong Zhuang, Tiehan Fan, Yinan Chen, Jian Li, Jian Yang, Ying Tai

llms agents reinforcement-learning

arXiv AI

Jul 21

CIGPO: Contextual Information-Gain Policy Optimization for Multi-Turn Evidence-Reading LLM Agents

arXiv:2607. 16244v1 Announce Type: cross Abstract: Training multi-turn evidence-reading agents with outcome-only reinforcement learning is unstable because intermediate turns receive little direct credit.

By Hao Dou

llms agents reinforcement-learning

arXiv AI

Aug 11

A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning

arXiv:2608. 08158v1 Announce Type: new Abstract: Sparse, delayed, and weakly informative rewards remain central obstacles to efficient reinforcement learning.

By Fouad Bahrpeyma

reinforcement-learning safety

Related stories

Meta-Learned Reward Shaping for Reinforcement Learning from Human Feedback

Predictable GRPO: A Closed-Form Model of Training Dynamics

Online Reward-Punishment Learning from Fixed-Channel Perceptual Event Streams without Environment Rewards

RoMeRL: Balancing Feedback Coverage and the Memory-Reward Trap in Self-Evolving Agent Memory via Reduced-Order Utility States

CIGPO: Contextual Information-Gain Policy Optimization for Multi-Turn Evidence-Reading LLM Agents

A Unified Framework for Dynamic Reward Shaping in Reinforcement Learning