Reinforcement learning

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

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arXiv Computation and Language

Oct 15

GroundGUI: GUI Grounding by High-Quality Data and Distillation-to-Reinforcement Learning

arXiv:2610.19100v1 Announce Type: cross Abstract: Constructing robust GUI agents relies on multimodal visual grounding: the ability to precisely map natural language intents to actionable on-screen c...

By Qingsong Xie, Jiahao Tian, Jiacheng Tang, Zhifeng Li, Madalina Ciobanu, Navan Preet Singh, Qingqing Mao, Ritankar Das

agents reinforcement-learning fine-tuning efficiency multimodal benchmarks

arXiv Computation and Language

Oct 15

Learning Perception to Action Rules through Reinforcement Learning for Theory-of-Mind Reasoning

arXiv:2610.19796v1 Announce Type: cross Abstract: In Theory-of-Mind (ToM) reasoning, large language models may correctly infer the agent's mental state but fail to use that inference when predicting...

By Soyoung Oh, Nursulu Sagimbayeva, Nicholas Jennings, Paulina Hussein, Mehwish Alam, Vera Demberg

llms agents reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Oct 15

IACM-RL: Intent-Aware Context Management and Reinforcement Learning for Complex Tool Invocation under Dynamic Intent Fluctuations

arXiv:2608.02110v2 Announce Type: replace Abstract: Executing long-horizon tool invocations in real-world environments is severely challenged by dynamic user intent noise. Existing methods attempt ro...

By Dingwei Zhu, Jiahan Li, Chengjun Pan, Yunxian Yang, Yunbin Zhao, Yunke Zhang, Zhonghang Lu, Zhuohui Sheng, Chenhao Huang, Jiahang Lin, Yajie Yang, Junlin Shang, Shichun Liu, Yuhui Wang, Honglin Guo, Junjie Ye, Xin Guo, Jiazheng Zhang, Ming Zhang, Shihan Dou, Zhiheng Xi, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang

reinforcement-learning efficiency

arXiv Computation and Language

Oct 15

When Is Enough Not Enough? Illusory Completion in Search Agents

arXiv:2602.07549v3 Announce Type: replace-cross Abstract: In agentic search, an LLM agent searches the web, reads the pages it finds, and decides what to look for next before returning an answer. But...

By Dayoon Ko, Jihyuk Kim, Sohyeon Kim, Haeju Park, Dahyun Lee, Gunhee Kim, Moontae Lee, Kyungjae Lee

llms agents reinforcement-learning

arXiv Computation and Language

Oct 15

Draft-Thinking: Learning Efficient Reasoning in Long Chain-of-Thought LLMs

arXiv:2603.00578v2 Announce Type: replace-cross Abstract: Long chain-of-thought~(CoT) reasoning improves the problem-solving capability of large reasoning models~(LRMs), but often incurs substantial...

By Jie Cao, Tianwei Lin, Juekai Lin, Zhenxuan Fan, Yunqi Cao, Bo Yuan, Ziyuan Zhao, Rolan Yan, Wenqiao Zhang, Siliang Tang

llms reinforcement-learning fine-tuning benchmarks

arXiv Computation and Language

Oct 15

Fish Audio Drama 3 Technical Report

arXiv:2610.18010v2 Announce Type: replace-cross Abstract: We introduce Fish Audio Drama 3, an open-source speech generation model that turns text into directed performances, built on a 35B-parameter...

By Dawei Han, Shidong Li, Shijia Liao, Jiahua Liu, Songting Liu, Qingzheng Wang, Yuxuan Wang, Xin Chen, Yifan Cheng, Tianyu Li, Gangin Park, Xuehao Yang, Chang Zeng, Haoyang Zhang, Ruoyi Zhang, Xiangyu Zhang

reinforcement-learning fine-tuning

arXiv Computer Vision

Oct 15

MMKG-R1: Constructing Multimodal Knowledge Graphs via Reinforcement Learning

arXiv:2610.19341v1 Announce Type: new Abstract: Multimodal knowledge graphs (MMKGs) organize information across different modalities to support multimodal Graph Retrieval-Augmented Generation (RAG),...

By Zongyu Wu, Yilong Wang, Xiaochen Wang, Minhua Lin, Yuan Zhong, Fenglong Ma, Xiang Zhang, Suhang Wang

llms rag reinforcement-learning multimodal

arXiv Computer Vision

Oct 15

BluePixel: Toward Native 4K Agentic Image Editing

arXiv:2610.19882v1 Announce Type: new Abstract: Instruction-driven image editing has reached native 4K, yet how such editors are trained remains undisclosed, and pretraining one from scratch is beyon...

By Shanyan Guan, Shiyu Li, Xin Li, Zhizhou Chen, Yunzhe Xu, Yuehao Liu, Ruixue Shen, Shiju Zhang, Min Jin, Qiang Liu, Junbo Liu, Penggang Shi, Sheng Sun, Xiao Wang, Yong Zhang, Zhongang Qi, Wei Li, Chao Ma, Ying Tai, Yanhao Ge

llms agents reinforcement-learning efficiency

arXiv Computer Vision

Oct 15

Instruction Mining From Images: Constructing a Synthetic Dataset for Multimodal Learning ILSP DCVLR Competition Submission

arXiv:2610.19932v1 Announce Type: new Abstract: This work presents a compact and reproducible pipeline for synthesizing and curating image-based instruction-response pairs for DCVLR. We draw from the...

By Eleni E. Kasoura, Dimitrios Damianos, Georgios Paraskevopoulos, Vassilios Katsouros

reinforcement-learning multimodal

arXiv Computer Vision

Oct 15

When Pairwise Preferences Cycle: A Reliability-Anchored Preference Walk for Video GRPO

arXiv:2610.20089v1 Announce Type: new Abstract: Pairwise preference feedback has become a widely adopted supervision signal for reinforcement learning in video generation models, as pairwise comparis...

By Xinrui Lin, Fan Li, Shumin Wang, Dehong Kong, Yanyong Zhang

diffusion reinforcement-learning

arXiv Statistics ML

Oct 15

Thompson Sampling for Learning in Zero-Sum Games

arXiv:2610.18899v1 Announce Type: cross Abstract: Thompson sampling (TS) achieves logarithmic and asymptotically optimal regret in classical stochastic bandits, but its behavior in multi-agent learni...

By Kenshi Abe, Kaito Ariu, Po-An Wang

agents reinforcement-learning

arXiv Machine Learning

Oct 15

More Methods, Better Agents? A Reality Check on Self-Distillation in Agentic Reinforcement Learning

arXiv:2610.19789v1 Announce Type: cross Abstract: Self-distillation in agentic reinforcement learning has produced a growing number of methods. However, existing evaluations often reuse published bas...

By Linhao Zhong, Bozhen Fang, Xuehao Wang, Duochao Shi, Zekai Luo, Guangming Yao, Yi Yuan, Hao Chen, Jingdong Chen, Chunhua Shen

agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Oct 15

Model What Matters: Decision-Guided Selective World Modeling for Language Agents

arXiv:2610.19642v1 Announce Type: cross Abstract: Reinforcement learning provides a direct mechanism for optimizing task objectives in large language model-driven agents, but task rewards are typical...

By Maolin Ran, Minda Chen, Di Huang, Wei Xiong, Fei Huang, Weiwen Liu

llms agents nlp reinforcement-learning benchmarks

arXiv AI

Oct 15

Optimizing Team Behavior via Extremum-seeking Control in Multi-agent Reinforcement Learning

arXiv:2610.18808v1 Announce Type: cross Abstract: In cooperative Multi-Agent Reinforcement Learning (MARL), agents must learn to coordinate their actions to achieve shared objectives. A key indicator...

By Svar Rajankumar Patel, Kshitij Jerath

agents reinforcement-learning

arXiv AI

Oct 15

Fast Rates for Offline Q-Learning with KL Regularization

arXiv:2610.18839v1 Announce Type: cross Abstract: This paper investigates offline Q-learning for finite-horizon Markov decision processes with Kullback-Leibler (KL) regularization. Recent studies hav...

By Yize Hao, Guangxin Jiang, Shuang Qiu

reinforcement-learning

arXiv AI

Oct 15

PopGen: Generating Semantically Diverse Populations for Zero-Shot Coordination

arXiv:2610.15969v1 Announce Type: new Abstract: In multi-agent reinforcement learning, exposing a cooperator to partners with diverse strategies during training is key to better collaboration at test...

By Paul Teiletche, Kanghyun Ryu, Kyungjoon Park, Dhruva Tirumala, Markus Wulfmeier, Negar Mehr

llms agents reinforcement-learning

arXiv AI

Oct 15

ClawLoop: Self-Improving Agents via Harness-Native Training Loop

arXiv:2610.14118v1 Announce Type: new Abstract: The rapid advancement of Large Language Models (LLMs) has driven their deployment toward general-purpose harness environments, where LLM-based agents o...

By Hansi Yang, Linji Hao, Hongyan Hao, An Wang, Han Wu, Qi Gu, Xunliang Cai

llms agents reinforcement-learning fine-tuning benchmarks data-engineering

arXiv AI

Oct 15

Visualizing the World Model in an Agent's Value Function

arXiv:2610.14186v1 Announce Type: new Abstract: Could model-free reinforcement learning algorithms actually be learning a model of the environment? In this work, we apply counterfactual interventions...

By Shawheen Naderi, Fatemeh Akherati, Yubei Chen

agents reinforcement-learning safety

arXiv AI

Oct 15

Process Advantage Signal Shaping: Advantage Construction for Process-Supervised Reinforcement Learning in LLM Reasoners

arXiv:2606.29296v2 Announce Type: replace Abstract: Dense process supervision supplies local feedback but leaves open how that feedback should become a policy advantage. We introduce PASS (Process Ad...

By Chao Wang, Hongtao Tian, Tao Yang, Yunsheng Shi, Ting Yao, Wenbo Ding

llms nlp reinforcement-learning efficiency benchmarks

arXiv AI

Oct 15

Getting Space Right: Agentic Rewards for Image Generation

arXiv:2610.14852v1 Announce Type: cross Abstract: Reinforcement learning (RL) is promising for spatial text-to-image generation, but existing work largely focuses on policy optimization, assuming acc...

By Xiaofeng Tan, Jiaxin Lin, Haobo Yuan, Tiancheng Shen, Zihan Su, Rui Chen, Hongsong Wang, Ming-Hsuan Yang

agents diffusion reinforcement-learning safety

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