Reinforcement learning

Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.

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arXiv AI

Aug 13

Is Per-Agent Policy Composition Safe? Rethinking Successor-Feature Transfer in Cooperative Multi-Agent Reinforcement Learning

arXiv:2608. 11658v1 Announce Type: cross Abstract: Many reinforcement learning systems, from fleet management to traffic signal control, must serve an objective that changes dynamically after deployment, and retraining a policy for each new objective is prohibitively expensive.

By Zijian Zhao, Sen Li

agents reinforcement-learning safety

arXiv AI

Aug 13

Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection

arXiv:2608. 11977v1 Announce Type: new Abstract: Tool-using LLM agents are commonly trained and evaluated in environments where tool calls succeed reliably, yet deployed tools can fail transiently, persistently, or silently.

By Chaoran Chen, Vy Nguyen, Ziji Zhang, Abhinav Gullapalli, Ziyi Wang, Yuxuan Lu, Dakuo Wang, Jing Huang, Zhou Yu, Jin Lai

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 13

Dueling Deep Q-Learning for Intrusion Detection

arXiv:2608. 11291v1 Announce Type: cross Abstract: Intrusion detection systems (IDS) and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods.

By Logan Luna (Georgia Institute of Technology), Matthew P. Berkowitz (Embry-Riddle Aeronautical University), Laxima Niure Kandel (Embry-Riddle Aeronautical University), Sirio Jansen-S'anchez (Embry-Riddle Aeronautical University)

reinforcement-learning benchmarks safety

arXiv AI

Aug 13

Teaching agentic AI to learn expert reasoning for rare disease diagnosis

arXiv:2606. 16149v3 Announce Type: replace Abstract: Rare disease diagnosis depends on expert reasoning that is scarce and difficult to transfer; off-the-shelf large language models (LLMs) rank the correct disease first in only 35.

By Minh-Ha Nguyen, Erica Gray, Bryce A. Schuler, Kevin W. Byram, Chih-Ting Yang, Fan Ma, Hua Xu, Wu-Chen Su, Chao Yan, Wei-Qi Wei, Adam Wright, Lisa Bastarache, Josh Peterson, Lingyao Li, Siyuan Ma, Undiagnosed Diseases Network, Rizwan Hamid, Thomas A. Cassini, Cathy Shyr

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 13

On the Definition of Intelligence

arXiv:2507. 22423v3 Announce Type: replace Abstract: To engineer AGI, we should first capture the essence of intelligence in a species-agnostic form that can be evaluated, while being sufficiently general to encompass diverse paradigms of intelligent behavior, including reinforcement learning, generative models, classification, analogical reasoning, and goal-directed decision-making.

By Kei-Sing Ng

reinforcement-learning safety

arXiv AI

Aug 13

Hamilton-Zero: A Neural Tensor-Network Foundation Model for Ground States of Arbitrary Quadratic Qubit Hamiltonians

arXiv:2608. 11911v1 Announce Type: cross Abstract: A central promise of useful quantum advantage is the ability to compute ground states of Hamiltonian systems beyond the reach of classical simulation methods.

By Timothy Heightman, Elena Orlova, Philip Mantrov, Aleksei Ustimenko

llms reinforcement-learning fine-tuning

arXiv AI

Aug 13

When the API Speaks the Wrong Language: Revisiting Post-Training for Multilingual Tool Use

arXiv:2608. 11715v1 Announce Type: cross Abstract: The reliability of Large Language Models (LLMs) for API calling degrades in multilingual settings.

By Siddharth Chauhan, Thomas Butler, Abhishek Singhania, Pankaj Porwal, Honey Gupta

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 13

GRPO for Financial Advice Generation: Outperforming Commercial LLMs under CATE Evaluation

arXiv:2608. 11787v1 Announce Type: cross Abstract: Generating actionable financial advice from business records demands that models integrate numerical reasoning, domain knowledge, and sound judgment, while avoiding recommendations that could harm the business.

By Ofir Ben Shoham, Shrutendra Harsola, Vignesh Subrahmaniam, Shravan Mohan, Yakov Gazman, Oded Vainas

llms nlp reinforcement-learning fine-tuning safety

arXiv Machine Learning

Aug 13

Diffusion-Guided Cooperative Policy Learning for Target Tracking Based on Underwater Mobile Agent Networks

arXiv:2603. 29426v2 Announce Type: replace-cross Abstract: Multi-agent reinforcement learning (MARL) provides a promising solution for cooperative target tracking in networks of autonomous underwater vehicles (AUVs).

By Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Zhenyu Wang, Chen An

agents diffusion reinforcement-learning

arXiv Machine Learning

Aug 13

LODESTAR: Trustworthy Entropy Is Navigated, Not Merely Measured -- Reinforced Polarizer Keeps a Frozen LLM from Being Confidently Misled by the Wrong Evidence

arXiv:2608. 11922v1 Announce Type: cross Abstract: Predictive-distribution entropy makes a strong selection rule in retrieval-augmented question answering: across five QA benchmarks, keeping the candidate answer that a frozen respondent LLM produces with the lowest answer-token entropy lifts mean answer $F_1$ from 0.

By Po-Jen Ko, Che-Cheng Wu, Hung-Chun Hsu, Li-Yang Chang, Chuan-Ju Wang

llms rag nlp reinforcement-learning benchmarks

arXiv AI

Aug 13

Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL

arXiv:2608. 11669v1 Announce Type: cross Abstract: Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks with no deterministic answer.

By Minglai Yang, Xinyu Guo, Utkarsh Tyagi, Mian Zhang, Razvan Dumitru, Sunjie Hou, Yunzhong He, Daniel Yue Zhang, Ying Liu

llms reinforcement-learning benchmarks safety

arXiv AI

Aug 13

Adversarial Resilience of Poisson-Process Submodular Maximization over Matroids: From Robust Offline Optimization to Full-Bandit Learning

arXiv:2608. 12134v1 Announce Type: cross Abstract: We study nonnegative submodular maximization subject to a general matroid when the offline algorithm is given an arbitrary controlled value oracle.

By Vaneet Aggarwal

reinforcement-learning safety

arXiv Machine Learning

Aug 13

IoT-Enabled Autonomous Maritime Navigation in Smart Ports: A Curriculum-Guided Shared Policy Learning Framework

arXiv:2608. 11597v1 Announce Type: cross Abstract: As smart port infrastructures increasingly rely on autonomous maritime devices enabled by the Internet of Things (IoT), ensuring reliable onboard navigation intelligence has become a critical challenge for safe and scalable operations in congested waterways.

By Yuqing Lin, Rangya Zhang, Kum Fai Yuen

agents reinforcement-learning

arXiv AI

Aug 13

DORA Explorer: Improving the Exploration Ability of LLMs Without Training

arXiv:2604. 17244v2 Announce Type: replace-cross Abstract: Large language model (LLM) agents for sequential decision-making struggle to produce diverse outputs.

By Priya Gurjar, Md Farhan Ishmam, Kenneth Marino

llms agents reinforcement-learning

arXiv AI

Aug 13

Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning

arXiv:2608. 11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals.

By Shibo Gao, Peipei Yang, Xu-Yao Zhang, Linlin Huang

llms agents reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Aug 13

Dynamic Governance of Multi-LLM Agent Systems for Collaborative Conversational Outcomes

arXiv:2608. 11207v1 Announce Type: new Abstract: When two LLM agents with structurally opposed objectives interact across multiple turns, the absence of a shared goal function produces not competition but collapse: the visitor capitulates, the site agent stops varying its approach, and the conversation terminates without achieving either agent's stated objective.

By Alexander Liss, Nicholas Desmond, Santiago Gil Gallego

llms agents reinforcement-learning safety

arXiv Machine Learning

Aug 13

Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving

arXiv:2608. 11498v1 Announce Type: cross Abstract: Natural-language-based scenario generation offers an intuitive means of describing rare and complex driving interactions, yet it is still uncertain whether training with language-structured data leads to truly adaptive control policies.

By Aditya Humnabadkar, Huaizhong Zhang, Ardhendu Behera

reinforcement-learning safety

arXiv AI

Aug 13

Agent Safety Should Be a Runtime Contract

arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.

By Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang

llms agents reinforcement-learning safety

arXiv AI

Aug 13

How to Spend Your Oracle Budget: Practical Guidance for Protein Structure Prediction Models

arXiv:2608. 12192v1 Announce Type: new Abstract: Foundation models for protein structure prediction remain unreliable on certain targets.

By Aleksandra Kalisz, Jack Simons, Krisztina Sinkovics, Noam Ghenassia, Shikha Surana, Henry Moss, Paul Duckworth

reinforcement-learning benchmarks

arXiv AI

Aug 13

Redistribution-based Cost Inference Improves Sparse Safe Offline RL

arXiv:2608. 12306v1 Announce Type: cross Abstract: Safe offline RL typically assumes access to dense per-step cost annotations, but in practice supervisors provide only trajectory-level stop-feedback: a binary signal at the first unsafe transition, with no per-step attribution.

By Ebenezer Gelo (University of the Witwatersrand), Geraud Nangue Tasse (University of the Witwatersrand), Steven James (University of the Witwatersrand), Benjamin Rosman (University of the Witwatersrand)

reinforcement-learning robotics

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