The reasoning process of Large Language Models (LLMs) is often plagued by hallucinations and missing facts in question-answering tasks. A promising solution is to ground LLMs' answers in verifiable knowledge sources, such as Knowledge Graphs (KGs). Prevailing KG-enhanced methods typically constrained LLM reasoning either by enforcing rules during generation or by imitating paths from a fixed set of demonstrations. However, they naturally confined the reasoning patterns of LLMs within the scope of prior experience or fine-tuning data, limiting their generalizability to out-of-distribution graph reasoning problems. To tackle this problem, in this paper, we propose Explore-on-Graph (EoG), a novel framework that encourages LLMs to autonomously explore a more diverse reasoning space on KGs. To incentivize exploration and discovery of novel reasoning paths, we propose to introduce reinforcement learning during training, whose reward is the correctness of the reasoning paths' final answers. To enhance the efficiency and meaningfulness of the exploration, we propose to incorporate path information as additional reward signals to refine the exploration process and reduce futile efforts. Extensive experiments on five KGQA benchmark datasets demonstrate that, to the best of our knowledge, our method achieves state-of-the-art performance, outperforming not only open-source but also even closed-source LLMs.
HiMed: Incentivizing Hindi Reasoning in Medical LLMs
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Indian Institute of Technology (Banaras Hindu University) Varanasi（印度理工学院（班加罗尔 Hindu 大学）瓦拉纳西分校）；Tongji University（同济大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；Shenzhen Loop Area Institute（深圳河套学院）；The Hong Kong University of Science and Technology（香港科技大学）；Halmstad University（哈尔姆斯塔德大学）
LogicReward: Incentivizing LLM Reasoning via Step-Wise Logical Supervision
National University of Singapore（新加坡国立大学）；University College London（伦敦大学学院）；University of Manchester（曼彻斯特大学）；University of Melbourne（墨尔本大学）；University of California, Santa Barbara（加州大学圣巴巴拉分校）
Countering Reward Over-optimization in LLM with Demonstration-Guided Reinforcement Learning
INRIA（法国国家信息与自动化研究所）；Cohere（Cohere公司）；DeepMind（DeepMind公司）；EHESS（法国高等社会科学研究院）；ENS-PSL（巴黎高等师范学院-巴黎文理研究大学）；CNRS（法国国家科学研究中心）；Meta AI Research（Meta人工智能研究院）
R1-ShareVL: Incentivizing Reasoning Capability of Multimodal Large Language Models via Share-GRPO
Nanyang Technological University（南洋理工大学）；ByteDance（字节跳动）；Tsinghua University（清华大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；The University of Sydney（悉尼大学）
KnowCoder-A1: Incentivizing Agentic Reasoning Capability with Outcome Supervision for KBQA
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；State Key Laboratory of AI Safety（人工智能安全国家重点实验室）；School of Computer Science, University of Chinese Academy of Sciences（中国科学院大学计算机科学学院）
WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Tongyi Lab, Alibaba Group（阿里云实验室）；Electronics and Telecommunications Research Institute（电子电信研究院）；Ant Group（蚂蚁集团）
Pixel Reasoner: Incentivizing Pixel-Space Reasoning with Curiosity-Driven Reinforcement Learning
University of Waterloo（多伦多大学）；Hong Kong University of Science and Technology（香港科技大学）；University of Science and Technology of China（中国科学技术大学）；Vector Institute（向量研究所）
A Simple "Motivation" Can Enhance Reinforcement Finetuning of Large Reasoning Models
Generative AI Lab, College of Computing and Data Science, Nanyang Technological University, Singapore（生成式人工智能实验室，计算与数据科学学院，南洋理工大学，新加坡）；Tongyi Lab, Alibaba Group（通义实验室，阿里巴巴集团）
Self-Compression of Chain-of-Thought via Multi-Agent Reinforcement Learning
Renmin University of China（中国人民大学）；Xiaohongshu Inc.（小红书公司）；Institute of Automation,Chinese Academy of Sciences（中国科学院自动化研究所）；University of Southern California（南加州大学）；Shandong University（山东大学）
