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 14

CityRiSE: Reasoning Urban Socio-Economic Status in Large Vision-Language Models via Reinforcement Learning

arXiv:2510. 22282v2 Announce Type: replace-cross Abstract: Urban socio-economic sensing plays a vital role in advancing global sustainable development goals.

By Tianhui Liu, Hetian Pang, Xin Zhang, Jie Feng, Pan Hui, Yong Li

llms reinforcement-learning multimodal

arXiv Machine Learning

Aug 14

SEAR: Sample Efficient Action Chunking Reinforcement Learning

arXiv:2603. 01891v2 Announce Type: replace Abstract: Action chunking improves exploration and accelerates value propagation in long-horizon reinforcement learning, but naively applying off-policy methods to the temporally extended action space at reduced decision frequency offsets these gains, leading to poor sample efficiency.

By C. F. Maximilian Nagy, Onur Celik, Emiliyan Gospodinov, Florian Seligmann, Weiran Liao, Aryan Kaushik, Gerhard Neumann

llms rag reinforcement-learning robotics benchmarks

arXiv Machine Learning

Aug 14

Towards Socially Compliant Navigation in Deep Reinforcement Learning via Proxemics-Based Reward Modeling

arXiv:2608. 12917v1 Announce Type: new Abstract: Developing effective robot navigation methods in crowded environments is essential for real-world applications.

By Takieddine Soualhi (CHROMA), Jacques Saraydaryan (CPE, CHROMA), Laetitia Matignon (UCBL)

reinforcement-learning robotics

arXiv AI

Aug 14

OGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways

arXiv:2608. 12995v1 Announce Type: new Abstract: Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints.

By Mao Jiayang, Wang Lanfeng, Peng Zhao-Han

agents reinforcement-learning

arXiv Machine Learning

Aug 14

Momentum as Residual-Driven Multiplier Correction for Deep Learning Optimization

arXiv:2608. 12925v1 Announce Type: new Abstract: Momentum-based optimizers are widely used in modern deep learning, yet the relations among momentum recursion, update geometry, and acceleration remain only partially understood.

By Zhixin Ren, Yau Lyu, Congrong Li, Liping Zhang, Shengbo Eben Li

reinforcement-learning

arXiv AI

Aug 14

Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning

arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.

By Irene Brugnara, Alessandro Valentini, Andrea Micheli

reinforcement-learning

arXiv Machine Learning

Aug 14

A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability

arXiv:2506. 04291v2 Announce Type: replace Abstract: With the proliferation of Internet of Things (IoT) devices, the demand for addressing complex optimization challenges has intensified.

By Wenhan Xu, Jiashuo Jiang, Lei Deng, Danny Hin-Kwok Tsang

reinforcement-learning benchmarks

arXiv Machine Learning

Aug 14

RoutePack: Expert Placement and Attention-Aware Data Packing for MoE Reinforcement Learning

arXiv:2608. 12146v1 Announce Type: cross Abstract: Training Mixture-of-Experts (MoE) models for reinforcement learning (RL) couples two load-balancing problems: sequence composition determines dense attention work in each data-parallel microbatch, while token routing determines sparse expert work on expert-parallel ranks.

By Yibo Shen, Xudong Han, Xiaowei Zhu, Gen Li, Zhenxuan Pan

reinforcement-learning efficiency

arXiv Machine Learning

Aug 14

Multi-AUV Ad-hoc network-based Target Tracking: A Value Gradient Guidance Multi-Agent Diffusion Reinforcement Learning Approach

arXiv:2608. 12436v1 Announce Type: new Abstract: Multi-AUV ad-hoc network-based target tracking requires networked autonomous underwater vehicles (AUVs) to cooperatively track maneuvering targets under constrained acoustic communication, dynamic topology, and uncertain ocean disturbances.

By Jiaao Ma, Chuan Lin, Guangjie Han, Shengchao Zhu, Qian Zhu, Ying Liu, Zhenyu Wang

agents diffusion reinforcement-learning

arXiv Machine Learning

Aug 14

Intern-S2-Preview: Scientific Agentic Foundation Model

arXiv:2608. 13505v1 Announce Type: new Abstract: Scientific discovery increasingly requires AI systems that can reason over scientific evidence of heterogeneous modalities, interact with scientific tools and environments, and sustain progress across long task horizons.

By Lei Bai, Jiaqi Cao, Chiyu Chen, Guanzhou Chen, Kai Chen, Guangran Cheng, Erfei Cui, Xuanlang Dai, Shengyuan Ding, Shangheng Du, Yanhui Duan, Yue Fan, Youqing Fang, Quan Gan, Yuanyuan Gao, Jiaye Ge, Lixin Gu, Yuzhe Gu, Qipeng Guo, Junjun He, Xin Hong, Ming Hu, Zhouqi Hua, Haian Huang, Junhao Huang, Zixian Huang, Minxi Jin, Lingkai Kong, Alexander Lam, Zehao Li, Zonglin Li, Tianhao Liang, Dahua Lin, Junyao Lin, Tianyang Lin, Zhouhan Lin, Jiangning Liu, Jin Liu, Kuikun Liu, Wenran Liu, Yifei Liu, Yuhong Liu, Yuhong Liu, Zhoumianze Liu, Ziyan Liu, Ziyu Liu, Haijun Lv, Han Lv, Chengqi Lyu, Le Ma, Ningsheng Ma, Zerun Ma, Haoyang Peng, Runyu Peng, Jifei Shan, Zixin Shang, Kou Shi, Xiang Shi, Qisheng Su, Xuerui Su, Hao Sun, Xiao Sun, Yanan Sun, Yu Sun, Huanze Tang, Yinghao Tang, Wenhui Tian, Zhongbo Tian, Bingli Wang, Haomin Wang, Jiarui Wang, Jingzhi Wang, Rui Wang, Xiquan Wang, Yi Wang, Zhecan Wang, Ziyi Wang, Zun Wang, Rubin Wei, Lianyi Wu, Wen Wu, Yue Wu, Yuhan Wu, Zhenyu Wu, Zijian Wu, Shuhao Xing, Jun Xu, Xingle Xu, Xuenan Xu, Xiangchao Yan, Ziang Yan, Bowen Yang, Danni Yang, Lin Yang, Zhiqi Yang, Qian Yao, Haochen Ye, Peng Ye, Jinhui Yin, Jiashuo Yu, Dingbo Yuan, Fei Yuan, Yuhang Zang, Bo Zhang, Chao Zhang, Chen Zhang, Hongjie Zhang, Junming Zhang, Wenlong Zhang, Wenwei Zhang, Yiming Zhang, Zhuo Zhang, Ziyang Zhang, Haiteng Zhao, Penghao Zhao, Yibo Zhao, Zhonghan Zhao, Zhihang Zhong, Bowen Zhou, Peiheng Zhou, Xin Zhou, Xinyu Zhou, Yunhua Zhou, Dongsheng Zhu, Yicheng Zou

agents reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv Machine Learning

Aug 14

Variance Reduction Based Experience Replay for Policy Optimization

arXiv:2602. 05379v2 Announce Type: replace-cross Abstract: Effective reinforcement learning (RL) for complex stochastic systems requires leveraging historical data to improve sample efficiency and accelerate policy optimization.

By Hua Zheng, Wei Xie, M. Ben Feng, Keilung Choy

reinforcement-learning benchmarks safety

arXiv AI

Aug 14

Cat-DPO: Category-Adaptive Safety Alignment

arXiv:2604. 17299v3 Announce Type: replace-cross Abstract: Aligning large language models with human preferences must balance two competing goals: responding helpfully to legitimate requests and reliably refusing harmful ones.

By Tiankai Yang, Yi Nian, Xinyuan Li, Ruiyao Xu, Henry Peng Zou, Kaize Ding, Xiyang Hu, Yan Liu, Yue Zhao

llms reinforcement-learning safety

arXiv Machine Learning

Aug 14

Revisiting Overestimation Bias Problem of Q-learning: Settling Large Discrete Action Space via Action Intersection

arXiv:2608. 12912v1 Announce Type: new Abstract: This paper considers the overestimation bias problem of Q-learning in the setting of a large action space, for the purpose of relieving the bottleneck of existing methods.

By Pu Li, Tao Tan, Hong Xie, Xiaoyu Shi, Mingsheng Shang

reinforcement-learning benchmarks safety

arXiv AI

Aug 14

AHD Agent: Agentic Reinforcement Learning for Automatic Heuristic Design

arXiv:2605. 08756v2 Announce Type: replace Abstract: Automatic heuristic design (AHD) has emerged as a promising paradigm for solving NP-hard combinatorial optimization problems (COPs).

By Haoze Lv, Ning Lu, Ziang Zhou, Yew-Soon Ong, Shengcai Liu

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Aug 14

Training and Benchmarking Code Generation for Physics-Inspired Animations

arXiv:2602. 10840v2 Announce Type: replace Abstract: Large language models (LLMs) have been widely studied in areas such as mathematical reasoning, complex coding, and scientific problem solving.

By Yanan Wang, Renxi Wang, Yongxin Wang, Xuezhi Liang, Fajri Koto, Timothy Baldwin, Xiaodan Liang, Haonan Li

llms reinforcement-learning multimodal benchmarks data-engineering

arXiv AI

Aug 14

Beyond the Best Guess: Improving LLM Solution Coverage with Evolution Strategies

arXiv:2608. 12679v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in discovery domains such as math and science.

By Conor F. Hayes, Elliot Meyerson, Kajetan Schweighofer, Roberto Dailey, Babak Hodjat, Risto Miikkulainen, Xin Qiu

llms reinforcement-learning benchmarks

arXiv AI

Aug 14

Spatial Memory Agent: Experience-Grounded Procedure Memory for Spatial Intelligence

arXiv:2608. 12743v1 Announce Type: new Abstract: Spatial intelligence is becoming a foundation for embodied agents, robotic planning, and multimodal assistants.

By Haokai Zhang, Yuhang Ding, Yunshu Zhou, Xinze Du, Shengtao Zhang, Zhiyue Zhao, Yuling Xi, Hao Chen

agents computer-vision reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv AI

Aug 14

Teach the Magnitude, Not the Direction: Verifier-Bounded Credit Assignment for Multi-Turn Multi-step LLM Agents

arXiv:2608. 13179v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) offers a verifier-bounded performance ceiling for training multi-turn tool-use agents, yet its trajectory-level credit assignment conflates heterogeneous per-turn outcomes into a single reward signal.

By Zechuan Wang, Siyuan Lu, Hongxuan Zhang, Linjian Mo, Chenyi Zhuang, Leilei Gan

llms agents reinforcement-learning efficiency

Hugging Face Trending Papers

Aug 14

Fashion Outfit Generation via Unified Sequential Composition Models

The task of synthesizing stylistically coherent fashion outfits from massive item libraries, known as fashion outfit generation, remains a non-trivial challenge, primarily due to the non-monotonic and implicit nature of aesthetic compatibility, coupled with the exponentially large combinatorial search space. In this paper, we formalize this task as Constrained Ensemble Generation (CEG) and model it as a finite-horizon deterministic Markov Decision Process.

reinforcement-learning benchmarks

Hugging Face Trending Papers

Aug 13

Behavioral Reprogramming of Open-Weights Models: Cognitive Plasticity and Alignment Bounds

Large language models (LLMs) are predominantly aligned to function as passive, sycophantic assistants. We challenge this default paradigm by empirically evaluating the cognitive plasticity of open-weight architectures when subjected to rigorous behavioral reprogramming.

llms reinforcement-learning fine-tuning benchmarks safety

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