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 8

Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms

arXiv:2607. 06489v1 Announce Type: new Abstract: The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions.

By Marcos Eduardo Cruz Victorio, Karl Mason

agents reinforcement-learning

arXiv AI

Jul 8

FootsiesGym: A Fighting Game Benchmark for Two-Player Zero-Sum Imperfect-Information Games

arXiv:2607. 06514v1 Announce Type: new Abstract: We present FootsiesGym, an open-source environment for learning in a non-trivial two-player, zero-sum, imperfect-information game.

By Chase McDonald, Nathan Tsang, Wesley N. Kerr

reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 8

Joint Energy Management and Coordinated AIGC Workload Scheduling for Distributed Data Centers: A Diffusion-Aided Reward Shaping Approach

arXiv:2605. 02965v2 Announce Type: replace Abstract: Artificial intelligence-generated content (AIGC) has emerged as a transformative paradigm for automating the creation of diverse and customized content, giving rise to rapidly growing computational workloads in cloud data centers.

By Yang Fu, Peng Qin, Liming Chen, Zihao Zhang, Hao Yu, Yifei Wang

diffusion reinforcement-learning efficiency benchmarks

arXiv AI

Jul 8

Catalyst Papers in Artificial Intelligence Research: A Landscape on ICLR from 2017 to 2025

arXiv:2607. 05401v1 Announce Type: cross Abstract: A small number of methodological contributions, including word2vec, the Transformer, large-scale pre-training, and reinforcement learning from human feedback, have reshaped NLP and AI research over the past decade.

By Fan Huang

llms rag nlp reinforcement-learning

arXiv Machine Learning

Jul 8

Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids

arXiv:2607. 05553v1 Announce Type: new Abstract: Transient stability control in smart grids requires rapid post-fault damping of generator frequency and rotor angle deviations to prevent cascading failures.

By Omar Al-Refai, Ibrahim Shahbaz, Adam Ali Husseinat, Eman Hammad

agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jul 8

Adaptive digital twins for predictive decision-making: Online Bayesian learning of transition dynamics

arXiv:2512. 13919v3 Announce Type: replace Abstract: This work shows how adaptivity can enhance value realization of digital twins in civil engineering.

By Eugenio Varetti, Matteo Torzoni, Marco Tezzele, Andrea Manzoni

reinforcement-learning

arXiv AI

Jul 8

CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration

arXiv:2607. 05465v1 Announce Type: cross Abstract: Complex image creation and editing often require more than a single generation or editing model.

By Hairui Zhu, Yiying Yang, Tengjin Weng, Ziyu Lu, Xiao Yao, Xiaoyang Ye, Lin Ma, Wenhao Jiang

agents reinforcement-learning robotics fine-tuning multimodal

arXiv Machine Learning

Jul 8

Entanglement as a Structural Complexity Axis: A PAC-Bayesian View of Generalization in Quantum Policies and Value Functions

arXiv:2607. 06230v1 Announce Type: cross Abstract: Parameterized quantum circuits (PQCs) are increasingly used as policies and value functions in quantum reinforcement learning, yet it remains unclear when and why quantum policies generalize.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

reinforcement-learning

arXiv AI

Jul 8

CONFLUX: A Latent Diffusion Model for 3D Chest-CT Synthesis with RL Post-Training

arXiv:2607. 02998v2 Announce Type: replace-cross Abstract: Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning.

By Max Van Puyvelde, Halil Ibrahim Gulluk, Wim Van Criekinge, Olivier Gevaert

llms diffusion reinforcement-learning

arXiv AI

Jul 8

A Definition and Roadmap for World Models

arXiv:2607. 06401v1 Announce Type: new Abstract: World models -- internal simulators that learn the structure and dynamics of an environment -- have become one of the most actively debated concepts in AI.

By Xinyuan Chen, Haoyu Guo, Shi Guo, Bingqi Jiang, Chunhua Shen, Xing Shen, Tianfan Xue, Yufei Xue, Mulin Yu, Weinan Zhang, Bin Zhao, Bowen Zhou, Ming Zhou

diffusion reinforcement-learning robotics

arXiv AI

Jul 8

From Passive Retrieval to Active Memory Navigation: Learning to Use Memory as a Structured Action Space

arXiv:2607. 05794v1 Announce Type: new Abstract: Long-term user memory is essential for personalized conversational agents, yet many memory systems still expose memory through passive retrieval interfaces, making the model a consumer of pre-selected evidence.

By Yue Xu, Yutao Sun, Yihao Liu, Mengyu Zhou, Jiayi Qiao, Lu Ma, Kai Tang, Wenjie Wang, Xiaoxi Jiang, Guanjun Jiang

agents reinforcement-learning

arXiv AI

Jul 8

PCBWorld: A Benchmark Environment for Engine-Grounded PCB Design Automation

arXiv:2607. 05915v1 Announce Type: new Abstract: PCB routing is the task of connecting the nets of a board with copper traces under strict design rules, yet learning-based methods still lag behind rule-based routers.

By Hyungseok Song, Junseok Park, Won-Seok Choi, Seohui Bae, Han-Seul Jeong, Youngjoon Park, Soonyoung Lee

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 8

Information Gain-based Rollout Policy Optimization: An Adaptive Tree-Structured Rollout Approach for Multi-Turn LLM Agents

arXiv:2607. 06223v1 Announce Type: new Abstract: Reinforcement learning has become a promising paradigm for improving large language model (LLM) agents on long-horizon search tasks, where the agent must make a sequence of intermediate decisions before receiving a final outcome.

By Yijun Zhang, Fan Xu, Jiaxin Ding, Yule Xie, Shiqing Gao, Xin Ding, Haoxiang Zhang, Luoyi Fu, Xinbing Wang

llms agents reinforcement-learning benchmarks

arXiv AI

Jul 8

Base Models Know How to Reason, Thinking Models Learn When

arXiv:2510. 07364v4 Announce Type: replace Abstract: What do thinking language models learn during training that their base models lack?

By Constantin Venhoff, Iv\'an Arcuschin, Philip Torr, Arthur Conmy, Neel Nanda

llms reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jul 8

HERB: Human-augmented Efficient Reinforcement learning for Bin-packing

arXiv:2504. 16595v2 Announce Type: replace-cross Abstract: Packing objects efficiently is a fundamental problem in logistics, warehouse automation, and robotics.

By Gojko Perovic, Nuno Ferreira Duarte, Atabak Dehban, Gon\c{c}alo Teixeira, Egidio Falotico, Jos\'e Santos-Victor

reinforcement-learning robotics

arXiv Machine Learning

Jul 8

Model-based Bootstrap of Controlled Markov Chains

arXiv:2605. 12410v2 Announce Type: replace-cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history-dependent control policies, a setting that arises naturally in offline reinforcement learning (RL) when the behavior policy generating the data is unknown.

By Ziwei Su, Imon Banerjee, Diego Klabjan

reinforcement-learning

arXiv Machine Learning

Jul 8

Contextual Procurement Auctions with Bandit Learning

arXiv:2607. 05813v1 Announce Type: cross Abstract: We study repeated contextual procurement auctions in which the platform must learn context-dependent product values from bandit feedback.

By Yiling Chen, Shi Feng, Sadie Zhao

reinforcement-learning

arXiv Machine Learning

Jul 8

6G Sensing Security: Distributed Game-Theoretic RL for Urban Beamforming and Attacker Detection

arXiv:2607. 06115v1 Announce Type: cross Abstract: In next-generation networks, communication systems will no longer be limited to data transmission and will be expected to acquire awareness of the surrounding environment.

By Parmida Geranmayeh, Onur G\"unl\"u

reinforcement-learning

arXiv Machine Learning

Jul 8

A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models

arXiv:2607. 05615v1 Announce Type: new Abstract: Activation steering via sparse autoencoders (SAEs) enables behavioral control of large language models without task-specific fine-tuning, but standard methods apply the steering signal at every generated token, incurring constant per-token perturbation that risks degrading fluency.

By Nima Eshraghi, Lovedeep Gondara, Yuqing Huang, Sagarika Suresh, Leizer Teran, Jithin Pradeep, Xiaotong Xu, Fanny Chevalier

llms reinforcement-learning fine-tuning

arXiv AI

Jul 8

Beyond Static Evaluation: Building Simulation Environments for Scalable Agentic Reinforcement Learning

arXiv:2607. 05773v1 Announce Type: new Abstract: As Large Language Models (LLMs) evolve into autonomous agents, traditional static evaluation fails to capture multi-step decision-making.

By Akshay Arora, Ishan Nigam, Ashutosh Aggarwal, Shefali Bansal, Krishna Singh, Sweta Kumari, Nikhil Mittal, Shariq Farhan, Siddarth Malreddy

llms agents reinforcement-learning

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