arXiv AI July 16, 2026 By Celeste Veronese, Edoardo Zorzi, Daniele Meli, Alessandro Farinelli

Explaining Reinforcement Learning Agents via Inductive Logic Programming

arXiv:2607. 13655v1 Announce Type: new Abstract: Explainable Reinforcement Learning (XRL) seeks to make Reinforcement Learning (RL) policies more transparent and interpretable, a key requirement in safety-critical and human-centric scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 7

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

arXiv:2507. 04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods.

By Saksham Sahai Srivastava, Vaneet Aggarwal

llms reinforcement-learning benchmarks safety

arXiv AI

Sep 24

DCRL: Decoupling and Coupling Reinforcement Learning via Policy-Reward Manifold Alignment

arXiv:2609.27572v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as a key paradigm for improving the reasoning capabilities of large language models (LLMs). However, existing...

By Henan Sun, Zehua Li, Haitao Hu, Qifan Zhang, Jianfeng Zhang, Nuo Chen, Jia Li

llms reinforcement-learning safety

arXiv AI

Jun 4

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

arXiv:2606. 04503v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has greatly advanced large reasoning models (LRMs), but it requires timely training on a huge fully-annotated dataset.

By Guangcheng Zhu, Shenzhi Yang, Haobo Wang, Xing Zheng, Yingfan MA, Xuening Feng, Zhongqi Chen, Bowen Song, Weiqiang Wang, Gang Chen

reinforcement-learning

arXiv Machine Learning

Aug 18

Temporal Logic Guided Universal Task Representations for Reinforcement Learning

arXiv:2608. 15509v1 Announce Type: cross Abstract: Task guided agents demonstrate strong performance in a wide range of complex tasks.

By Hao Zhang, Zhangli Zhou, Zhen Kan

agents reinforcement-learning robotics

arXiv Machine Learning

Sep 23

Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy

arXiv:2509.00961v3 Announce Type: replace-cross Abstract: Active learning is a general learning mechanism shared by artificial and human learners. Whether AI can teach humans such a strategy that tra...

By Lun Ai, Johannes Langer, Ute Schmid, Stephen Muggleton

llms

arXiv AI

Jun 19

Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation

arXiv:2606. 19632v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets.

By Ahmad Farooq, Kamran Iqbal

agents reinforcement-learning robotics efficiency safety

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Related stories

A Technical Survey of Reinforcement Learning Techniques for Large Language Models

DCRL: Decoupling and Coupling Reinforcement Learning via Policy-Reward Manifold Alignment

Smart Picks in the Dark: Towards Efficient RLVR for Reasoning via Tracing Metacognitive Pivots

Temporal Logic Guided Universal Task Representations for Reinforcement Learning

Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy

Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation