Reinforcement learning

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

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arXiv Machine Learning

Jul 22

Reinforcement Learning for Delivery Drone-Based Participatory Sensing in Dynamic Environments

arXiv:2607. 18874v1 Announce Type: new Abstract: Using Unmanned Aerial Vehicle (UAV) for urban sensing has emerged as a powerful paradigm to monitor the status of the city, e.

By Xin Ouyang, Songxin Lei, Xusen Guo, Yutian Jiang, Sijie Ruan, Yuxuan Liang

rag reinforcement-learning robotics

arXiv AI

Jul 22

Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority

arXiv:2607. 18286v1 Announce Type: cross Abstract: Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic and avoiding extreme waits for a subset of vehicles.

By Philip-Roman Adam, Stefanie Schmidtner

reinforcement-learning

arXiv Machine Learning

Jul 22

S3: Stable Subgoal Selection by Constraining Uncertainty of Coarse Dynamics in Hierarchical Reinforcement Learning

arXiv:2607. 19232v1 Announce Type: new Abstract: Hierarchical Reinforcement Learning (HRL) intends to separate strategic planning from primitive execution.

By Kshitij Kumar Srivastava, Kshitij Jerath

agents reinforcement-learning benchmarks

arXiv Machine Learning

Jul 22

Multi-Timescale Latent-Action DRL for Joint Optimization in Edge-Cloud Networks

arXiv:2607. 18288v1 Announce Type: new Abstract: Load imbalance across edge and cloud layers degrades latency performance in hierarchical edge-cloud computing (HECC) systems under dynamic task arrivals and heterogeneous resources, leading to severe queuing delays and inefficient resource utilization.

By Vo Phi Son, Van-Dinh Nguyen, Ngoc Hung Nguyen, Trinh Van Chien, Symeon Chatzinotas

reinforcement-learning

arXiv AI

Jul 22

Reasoning Before Translation: Enhancing Legal Machine Translation with Structured Reasoning

arXiv:2607. 19181v1 Announce Type: cross Abstract: Neural machine translation (NMT) in the legal domain is a linguistically and conceptually demanding task, primarily due to the complexity of legal language and the high level of precision it requires.

By Aixiu An, Michael Jungo, Eloi Eynard, Mark Drenhaus, Andreas Fischer, Jean Hennebert, S\'ebastien Rumley

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 22

Deep Reinforcement Learning to Master the Asymmetric Strategy of Baghchal

arXiv:2607. 18296v1 Announce Type: new Abstract: Baghchal is a two-player asymmetric board game with Nepali origins where four tigers are to capture goats and twenty goats desire to keep tigers in immobility.

By Ranjit Raut, Aarav Subedi, Sagun Rai, Aaryan Shakya, Manoj Shakya

reinforcement-learning

arXiv AI

Jul 22

ISO: An RLVR-Native Optimization Stack

arXiv:2607. 19331v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) is rapidly advancing the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-space updates remains poorly understood.

By Hanqing Zhu, Wenyan Cong, Zhizhou Sha, Sagnik Mukherjee, Xinyuan Song, David Gonz\'alez-Mart\'inez, Xiaoxia Wu, Yuandong Tian, Shiwei Liu, David Z. Pan, Zhangyang "Atlas" Wang

llms reinforcement-learning efficiency

arXiv AI

Jul 22

From Trajectories to Instructions: Language-Conditioned Meta-Reinforcement Learning

arXiv:2607. 18830v1 Announce Type: cross Abstract: Model-Agnostic Meta-Learning (MAML) is a widely used framework for reinforcement learning (RL) that enables efficient transfer by learning global policy parameters that can be rapidly adapted to new tasks.

By Garvit Singla, Uma Maheswari Natarajan, Raghuram Bharadwaj Diddigi

rag reinforcement-learning benchmarks

arXiv AI

Jul 22

Intelligent Multi-UAV Navigation in ITNTNs: A Hierarchical LLM Approach

arXiv:2607. 18604v1 Announce Type: cross Abstract: The deployment of high-speed Uncrewed Aerial Vehicles (UAVs) in 3D aerial highways necessitates robust coordination of physical flight kinematics and multi-tier network handovers.

By Zijiang Yan, Hao Zhou, Wael Jaafar, Jianhua Pei, Ping Wang, Halim Yanikomeroglu, Hina Tabassum

llms agents reinforcement-learning efficiency

arXiv Machine Learning

Jul 22

Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures

arXiv:2607. 18359v1 Announce Type: cross Abstract: Critical infrastructures are increasingly distributed, interdependent, and exposed to evolving disruptions, making resilience a central requirement for their operation and control.

By Minghui Ding, Evangelos Pournaras

agents reinforcement-learning safety

arXiv AI

Jul 22

Inference-Time Steering for Cross-Lingual Factual Consistency in LLMs

arXiv:2607. 19243v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) demonstrate remarkable multilingual fluency, their internal knowledge representations remain disproportionately biased toward high-resource languages.

By Alexander Manev

llms reinforcement-learning robotics benchmarks safety

arXiv Machine Learning

Jul 22

TD-DPO: Difference-Aware Preference Optimization for Mitigating Sycophancy in Clinical Autism Intervention Dialogue

arXiv:2607. 18304v1 Announce Type: new Abstract: The sycophancy of large language models can increase the safety risk in intervention dialogue for autistic children.

By Shuzhong Lai, Junhong Lai, Chenxi Li, Qing Zhou, Haifeng Li, Gang Pan, Lin Yao, Yueming Wang

llms reinforcement-learning fine-tuning safety

arXiv AI

Jul 22

S2T-RLHF: Hierarchical Credit Assignment for Stable Preference-Based RLHF

arXiv:2607. 18258v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) with preference-based reward models often exhibits unstable training dynamics.

By Wei Chen, Guanghui Zhu, Yafei Li, Limin Wang, Yihua Huang

reinforcement-learning safety

arXiv AI

Jul 22

Off-Context GRPO: Learning to Reason on Hard Problems using Privileged Information

arXiv:2607. 19313v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) improves reasoning in large language models.

By Priyank Agrawal, Ankur Samanta, Shervin Ghasemlou, Jalaj Bhandari, Kavosh Asadi, Daniel Jiang, Aditya Modi

llms reinforcement-learning benchmarks

arXiv AI

Jul 22

Fishing Out Free Riders: Shapley-Based Reward Attribution for Parallel Reasoning via Reinforcement Learning

arXiv:2607. 18979v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at multi-step reasoning, yet current parallel reasoning approaches often fail to distinguish the contributions of individual reasoning paths.

By Wentao Zhang, Haoyu Zhang, Xinke Jiang, Yuxuan Cheng, Yuhan Pan, Miao Li, Zhipeng Qiao, Tao Feng, Zhen Tao, Dengji Zhao

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jul 22

H$^2$SD: Hybrid Hindsight Self-Distillation

arXiv:2607. 18955v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved the reasoning capabilities of large language models on tasks such as mathematical reasoning and code generation.

By Qiye Cai, Yichuan Ma, Linyang Li, Peiji Li, Yongkang Chen, Qipeng Guo, Yicheng Zou, Tao Gui, Xiaocheng Feng, Bing Qin

llms reinforcement-learning efficiency benchmarks

arXiv AI

Jul 22

Toward Learning POMDPs Beyond Full-Rank Actions and State Observability

arXiv:2601. 18930v4 Announce Type: replace-cross Abstract: We are interested in enabling autonomous agents to learn and reason about systems with hidden states, such as locking mechanisms.

By Seiji Shaw, Travis Manderson, Chad Kessens, Nicholas Roy

agents reinforcement-learning

arXiv Machine Learning

Jul 22

Stale but Stable: Staleness-Adaptive Trust Regions for Stabilizing Asynchronous Reinforcement Learning

arXiv:2607. 18722v1 Announce Type: new Abstract: Asynchronous reinforcement learning improves throughput by decoupling rollout generation from optimization, but staleness is an inevitable byproduct compounded by policy lag, engine delays, and mixture-of-experts routing.

By Junyao Yang, Yucheng Shi, Zongxia Li, Zhongzhi Li, Ruhan Wang, Xiangxin Zhou, Kishan Panaganti, Haitao Mi, Leowei Liang

reinforcement-learning efficiency

arXiv AI

Jul 22

Athena-Brain Technical Report: An Efficient Robot Brain for General Intelligence and Embodied Interactio

arXiv:2607. 18985v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated remarkable capabilities in language understanding, reasoning, and world knowledge.

By Jialian Li, Junhong Liu, Yuchen Cao, Weiran Guo, Jiaming Song, Xutao Wang, Yi Zhao, Jiangpin Liu, Jie Chen

llms agents reinforcement-learning robotics fine-tuning benchmarks

arXiv AI

Jul 22

Active Electrosensing and Communication in MARL-trained Weakly Electric Fish Collectives

arXiv:2511. 08436v2 Announce Type: replace-cross Abstract: How complex collective behavior emerges from individual interactions is a fundamental scientific question, but experimental cost and difficulty of simultaneous multi-brain recordings limit direct study in animals.

By Satpreet H. Singh, Sonja Johnson-Yu, Zhouyang Lu, Aaron Walsman, Federico Pedraja, Denis Turcu, Pratyusha Sharma, Naomi Saphra, Nathaniel B. Sawtell, Kanaka Rajan

agents reinforcement-learning

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