Reinforcement learning

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

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arXiv AI

Jul 17

Semantic Audio-driven Understanding for Dynamic Humanoid Whole Body Control

arXiv:2607. 14182v1 Announce Type: cross Abstract: Recent advances in humanoid robotics and reinforcement learning have enabled the acquisition of highly expressive whole-body motion policies.

By J. M. A. Marcelo, M. Brienza, E. Bugli, L. Comito, D. Nardi, D. D. Bloisi, V. Suriani

rag agents reinforcement-learning robotics multimodal safety

arXiv AI

Jul 16

AI in Cyberpsychology: A systematic literature review of Cybersecurity enhancement by using AI for analyzing psychology of Victims, Attackers, and Defenders

arXiv:2607. 13123v1 Announce Type: cross Abstract: Cybersecurity is the practice of protecting systems, networks, and data from digital attacks.

By Georg Thamer Francis, Malek Malkawi, Sevim Ey\"upo\u{g}lu, Reda Alhajj, Selim Akyoku\c{s}

nlp reinforcement-learning

arXiv Machine Learning

Jul 16

DAGR: State-Conditioned Goal Representations via Difference-Aware Goal Cross-Attention

arXiv:2607. 13731v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning hinges on how the goal is encoded.

By Xing Lei, Wenyan Yang, Xuetao Zhang, Donglin Wang

rag reinforcement-learning robotics safety

arXiv Machine Learning

Jul 16

From Novice to Expert: Cost-Aware Bandits for Evolving Worker Performance in Crowdsensing

arXiv:2607. 13546v1 Announce Type: new Abstract: Mobile crowdsensing (MC) recruits mobile users to perform sensing tasks using their smartphones, enabling large-scale applications such as traffic monitoring and environmental sensing.

By Yin Huang, Qingsong Liu, Jie Xu

reinforcement-learning

arXiv AI

Jul 16

OvisOCR2 Technical Report

arXiv:2607. 13639v1 Announce Type: cross Abstract: We introduce OvisOCR2, a 0.

By Shiyin Lu, Yinglun Li, Yu Xia, Yuhui Chen, An-Yang Ji, Jun-Peng Jiang, Qing-Guo Chen, Jianshan Zhao, En Lin, Haijun Li, Cheng Qin, Zhao Xu, Weihua Luo

reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 16

Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners

arXiv:2607. 13274v1 Announce Type: cross Abstract: Reinforcement learning is increasingly being considered for controlling real-world systems, from fusion plasma and autonomous vehicles to drug discovery and drinking water treatment, where reliability is essential and tuning budgets are limited.

By Haseeb Shah, Lingwei Zhu, Adam White, Martha White

agents reinforcement-learning

arXiv Machine Learning

Jul 16

A-IC3: Learning-Guided Adaptive Inductive Generalization for Hardware Model Checking

arXiv:2604. 21688v2 Announce Type: replace-cross Abstract: The IC3 algorithm represents the state-of-the-art (SOTA) hardware model checking technique, owing to its robust performance and scalability.

By Xiaofeng Zhou, Guangyu Hu, Hongce Zhang, Wei Zhang

agents reinforcement-learning benchmarks

arXiv AI

Jul 16

Multi-Agent Collaborative Reasoning with Tool-Augmented Evidence for Urban Region Profiling

arXiv:2607. 13558v1 Announce Type: new Abstract: Urban region profiling constitutes a core problem in urban computing, supporting applications such as population estimation, economic assessment, and environmental monitoring.

By Xixuan Hao, Yutian Jiang, Jiabo Liu, Yihang Yang, Guangyin Jin, Song Gao, Yuxuan Liang

rag agents reinforcement-learning multimodal

arXiv Machine Learning

Jul 16

Factorized Spectral Representations for Reinforcement Learning

arXiv:2607. 13498v1 Announce Type: new Abstract: Learning a compact model of the world from interaction data is central to sample-efficient deep reinforcement learning.

By Junyi Wu, Dan Li

reinforcement-learning robotics

arXiv AI

Jul 16

LLM-Guided Reinforcement Learning for Audio-Visual Speech Enhancement

arXiv:2603. 13952v3 Announce Type: replace-cross Abstract: In existing Audio-Visual Speech Enhancement (AVSE) methods, objectives such as Scale-Invariant Signal-to-Noise Ratio (SI-SNR) and Mean Squared Error (MSE) are widely used; however, their correlation with perceived speech quality is often suboptimal and provides limited interpretability for optimization.

By Chih-Ning Chen, Jen-Cheng Hou, Hsin-Min Wang, Shao-Yi Chien, Yu Tsao, Fan-Gang Zeng

llms nlp reinforcement-learning fine-tuning multimodal safety

arXiv AI

Jul 16

Agile perceptive multi-skill locomotion for quadrupedal robots in the wild

arXiv:2607. 13579v1 Announce Type: cross Abstract: Enabling quadrupedal robots to traverse complex terrains-from rugged outdoor environments to urban landscapes-requires seamless integration of multiple motor skills, smooth transitions between gaits, and high-speed perceptive locomotion using only onboard sensors.

By Jun-Gill Kang, Jaehyun Park, Tae-Gyu Song, Joon-Ha Kim, Seungwoo Hong, Hae-Won Park

llms agents reinforcement-learning robotics

arXiv Machine Learning

Jul 16

Pretraining in Actor-Critic Reinforcement Learning for Locomotion

arXiv:2510. 12363v4 Announce Type: replace-cross Abstract: The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years.

By Jiale Fan, Andrei Cramariuc, Tifanny Portela, Marco Hutter

reinforcement-learning robotics fine-tuning benchmarks

arXiv Machine Learning

Jul 16

Optimal and Efficient Contextual Combinatorial Semi-bandits with General Function Approximation

arXiv:2607. 13686v1 Announce Type: new Abstract: We study the contextual combinatorial semi-bandit (CCSB) problem with general reward function approximation.

By Hao Qin, Chicheng Zhang

reinforcement-learning benchmarks

arXiv AI

Jul 16

Groc-PO: Grounded Context Preference Optimization for Truthful Multimodal LLMs

arXiv:2607. 13712v1 Announce Type: cross Abstract: Despite the rapid progress of Multimodal Large Language Models (MLLMs), they still suffer from untruthfulness issues, such as visual hallucinations, content fabrication, and unfaithful reasoning, which substantially undermine their faithfulness and practical utility.

By Zhixiao Zheng, Zheren Fu, Zhiyuan Yao, Chunxiao Liu, Dongming Zhang, Zhendong Mao

llms reinforcement-learning multimodal safety

arXiv AI

Jul 16

Active Beyond-Diagonal RIS Empowered Heterogeneous Edge Computing: A Distributional Reinforcement Learning Approach

arXiv:2607. 13160v1 Announce Type: cross Abstract: Active beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) enables hybrid transmitting and reflecting mode to achieve effective signal amplification and full-space coverage, thus providing a promising solution for blockage-aware uplink offloading in heterogeneous mobile edge computing (MEC) systems.

By Tianyu Pang, Hongyu Li

reinforcement-learning

arXiv AI

Jul 16

Price of Fairness in Bandits: A Tight Minimax Characterization

arXiv:2607. 13402v1 Announce Type: cross Abstract: In bandit problems, standard regret-minimizing algorithms treat exploration as an amortized cost, which can expose early participants to unfair ex-ante losses in settings such as clinical trials.

By Dhruv Sarkar, Soumyadeep Dutta, Sayak Ray Chowdhury

reinforcement-learning safety

arXiv Machine Learning

Jul 16

Flow-aware Optimal Navigation in Unsteady Flows through Reinforcement Learning

arXiv:2607. 13553v1 Announce Type: cross Abstract: Autonomous robotic navigation in nonstationary time-varying fluid flows remains a fundamental challenge due to partial observability and the unpredictability of realistic environments.

By Andrea Maria Braghin, Nicol\`o Botteghi, Matteo Tomasetto, Andrea Manzoni, Gabriele Cazzulani

agents reinforcement-learning robotics

arXiv AI

Jul 16

Autonomous UAV Route Planning for Coverage Maximization in Environmental Monitoring: A Systematic Literature Review

arXiv:2607. 13054v1 Announce Type: cross Abstract: Environmental monitoring with unmanned aerial vehicles (UAVs) requires route planning methods that maximize covered area while handling energy limits, operational constraints, and geometric complexity.

By Sebastian Jouannet-Contreras, Carola Figueroa-Flores

agents reinforcement-learning

arXiv AI

Jul 16

LAPO: Leave-One-Turn Attribution for Self-Generated Process Rewards in Multi-Turn Search Reasoning

arXiv:2607. 13501v1 Announce Type: new Abstract: Reinforcement learning for multi-turn search reasoning typically relies on terminal outcome rewards, which cannot distinguish useful, redundant, and harmful intermediate interactions.

By Qiang Zhu, Jiajun Wu

llms agents reinforcement-learning

arXiv AI

Jul 16

Memory as a Controlled Process: Learned Adaptive Memory Management for LLM Agents

arXiv:2607. 13591v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly rely on external memory systems to accumulate experience across tasks.

By Eric Hanchen Jiang, Zhi Zhang, Yuchen Wu, Levina Li, Dong Liu, Xiao Liang, Rui Sun, Yubei Li, Edward Sun, Haozheng Luo, Zhaolu Kang, Aylin Caliskan, Kai-Wei Chang, Ying Nian Wu

llms agents reinforcement-learning benchmarks

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