Reinforcement learning

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

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arXiv AI

Jun 2

Stop Wandering, Find the Keys: LLMs Discriminate Key States for Efficient Multi-Agent Exploration

arXiv:2410. 02511v2 Announce Type: replace Abstract: With expansive state-action spaces, efficient multi-agent exploration remains a longstanding challenge in reinforcement learning.

By Yun Qu, Boyuan Wang, Yuhang Jiang, Jianzhun Shao, Yixiu Mao, Heming Zou, Chang Liu, Cheems Wang, Meiqin Liu, Xiangyang Ji

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 2

Latent Reasoning in TRMs is Secretly a Policy Improvement Operator

arXiv:2511. 16886v5 Announce Type: replace-cross Abstract: Recently, small models with latent recursion have obtained promising results on complex reasoning tasks.

By Arip Asadulaev, Rayan Banerjee, Fakhri Karray, Martin Takac

diffusion reinforcement-learning

arXiv Machine Learning

Jun 2

From Reward-Free Representations to Preferences: Rethinking Offline Preference-Based Reinforcement Learning

arXiv:2606. 01123v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) avoids explicit reward engineering by learning from pairwise human preference feedback.

By Jun-Jie Yang, Chia-Heng Hsu, Kui-Yuan Chen, Ping-Chun Hsieh

reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 2

B-GRTO: Bootstrapped Group Relative Tool Optimization for Referring Segmentation

arXiv:2605. 23500v2 Announce Type: replace-cross Abstract: Segmentation is a fundamental task in computer vision, underpinning pixel-level scene understanding and serving as a cornerstone for applications ranging from autonomous perception to medical image analysis.

By Mario Markov (INSAIT, Sofia University "St. Kliment Ohridski"), Stefan Maria Ailuro (INSAIT, Sofia University "St. Kliment Ohridski"), Mohammad Mahdi (INSAIT, Sofia University "St. Kliment Ohridski"), Luc Van Gool (INSAIT, Sofia University "St. Kliment Ohridski"), Danda Pani Paudel (INSAIT, Sofia University "St. Kliment Ohridski")

llms agents computer-vision reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jun 2

The Representation-Rationalizability Tradeoff in Reward Learning

arXiv:2606. 00291v1 Announce Type: cross Abstract: In RLHF, each training example contains a prompt $x$ and two candidate responses $y,y'$, and annotators provide pairwise preferences between these responses.

By Jing Dong, Yaoliang Yu, Pascal Pourpart

rag reinforcement-learning

arXiv AI

Jun 2

LLM4Cov: Execution-Aware Agentic Learning for High-coverage Testbench Generation

arXiv:2602. 16953v3 Announce Type: replace Abstract: Execution-aware LLM agents offer a promising paradigm for learning from tool feedback, but such feedback can be expensive and slow to obtain, making online reinforcement learning (RL) less practical in certain scenarios.

By Hejia Zhang, Zhongming Yu, Chia-Tung Ho, Haoxing Ren, Brucek Khailany, Jishen Zhao

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 2

Margin Adaptive DPO: Leveraging Reward Model for Granular Control in Preference Optimization

arXiv:2510. 05342v2 Announce Type: replace-cross Abstract: Direct Preference Optimization (DPO) has emerged as a simple and effective method for aligning large language models.

By Hyung Gyu Rho

llms nlp reinforcement-learning

arXiv AI

Jun 2

TInR: Exploring Tool-Internalized Reasoning in Large Language Models

arXiv:2604. 10788v2 Announce Type: replace-cross Abstract: Tool-Integrated Reasoning (TIR) has emerged as a promising direction by extending Large Language Models' (LLMs) capabilities with external tools during reasoning.

By Qiancheng Xu, Yongqi Li, Fan Liu, Hongru Wang, Min Yang, Wenjie Li

llms reinforcement-learning fine-tuning safety

arXiv AI

Jun 2

Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

arXiv:2606. 02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control.

By Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy

agents reinforcement-learning fine-tuning

arXiv AI

Jun 2

Two-Fidelity Best-Action Identification for Stochastic Minimax Tree

arXiv:2606. 01708v1 Announce Type: cross Abstract: We study fixed-confidence best-action identification (BAI) in stochastic minimax trees.

By Peter Chen, Xi Chen

llms reinforcement-learning

arXiv AI

Jun 2

Capability Self-Assessment: Teaching LLMs to Know Their Limits

arXiv:2606. 00251v1 Announce Type: new Abstract: The ability to recognize one's own limitations and decide whether to solve a problem or delegate is fundamental for reliable intelligent systems.

By Haoyan Yang, Reza Shirkavand, Yukai Jin, Jiawei Zhou, Shangqian Gao, Heng Huang

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 2

Randomized Least Squares Value Iteration itself is Joint Differentially Private

arXiv:2606. 01952v1 Announce Type: new Abstract: As reinforcement learning (RL) increasingly applies to sensitive domains, such as health care and recommendation systems, privacy-preserving techniques have become essential to protect users' sensitive information.

By Haiyang Lu, Pratik Gajane, Shaojie Bai, Mohammad Sadegh Talebi

reinforcement-learning safety

arXiv AI

Jun 2

CAREAgent: Clinical Agent with Structured Reasoning and Tool-Integrated for Order Generation

arXiv:2606. 01094v1 Announce Type: new Abstract: Clinical order generation serves as a critical bridge between clinical decision-making and real-world practice, translating medical decisions into concrete and executable orders.

By Ruihui Hou, Ziyue Huai, Chennuo Zhang, Ziyan Liu, Siran Zhao, Yao Yu, Jie Zhai, Tong Ruan

agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 2

TriAlign: Towards Universal Truth Consistency in Personalized LLM Alignment

arXiv:2606. 01755v1 Announce Type: new Abstract: Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsistencies across social groups, where some groups systematically receive less accurate responses on objective tasks.

By Thi-Nhung Nguyen, Linhao Luo, Rollin Omari, Junae Kim, Thuy-Trang Vu, Dinh Phung

llms agents reinforcement-learning benchmarks safety

arXiv Machine Learning

Jun 2

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

arXiv:2508. 02812v3 Announce Type: replace Abstract: Causal graphical models can encode large amounts structural knowledge, both from the background knowledge of domain experts and the structural knowledge discovered from randomized experiments or observational data.

By Katherine Avery, Chinmay Pendse, David Jensen

reinforcement-learning

arXiv AI

Jun 2

Community-Aware Assessment of Social Textual Engagement and Resonance: A Human-Centric Perspective on User-Generated Content Evaluation

arXiv:2606. 01897v1 Announce Type: new Abstract: Traditional Video Quality Assessment (VQA) focuses narrowly on aesthetic fidelity, overlooking the complex social dynamics that define quality in User-Generated Content (UGC).

By Tianjiao Li, Kai Zhao, Xiang Li, Yang Liu, Huyang Sun

reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 2

Learning When Not to Act: Mitigating Tool Abuse in Agentic Reinforcement Learning

arXiv:2606. 02132v1 Announce Type: new Abstract: Agentic reinforcement learning can induce tool abuse, where models overuse external tools even for queries solvable by internal reasoning.

By Liuji Chen, Dianxing Tang, Xing Shi, Dingshuo Chen, Qiang Liu, Shu Wu, Liang Wang

agents reinforcement-learning benchmarks

arXiv AI

Jun 2

CARE-RL: Capability-Aware Reinforcement Learning for Mitigating Cross-Domain Conflicts

arXiv:2606. 00609v1 Announce Type: cross Abstract: Reinforcement learning (RL) with verifiable rewards has achieved strong progress in reasoning-oriented LLMs, but extending it to multi-domain RL remains challenging due to reward unreliability in non-verifiable tasks and capability interference across domains.

By Rui Zhang, Xinle Wu, Yao Lu

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 2

Coherent Off-Policy Improvement of Large Behavior Models with Learned Rewards

arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.

By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters

reinforcement-learning robotics fine-tuning

arXiv AI

Jun 2

BAGEN: Are LLM Agents Budget-Aware?

arXiv:2606. 00198v1 Announce Type: cross Abstract: While agents are increasingly spending more resources, today agent cost is mostly measured only after execution.

By Yuxiang Lin, Zihan Wang, Mengyang Liu, Yuxuan Shan, Longju Bai, Junyao Zhang, Xing Jin, Boshan Chen, Jinyan Su, Xingyao Wang, Jiaxin Pei, Manling Li

llms agents reinforcement-learning fine-tuning

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