Reinforcement learning (RL) is promising for spatial text-to-image generation, but existing work largely focuses on policy optimization, assuming accurate rewards and effective online rollouts. We observe in-domain gains with out-of-domain degradation, exposing two limitations: unreliable spatial rewards in open-world settings and rollouts lacking basic-to-complex progression. We propose a curriculum-guided RL framework. First, we introduce SpatialCritic, an agentic reward model equipped with a spatial perception harness that combines reusable verification guidance with specialized vision tools. We then construct spatial preference datasets through targeted counterfactual perturbations and perform agent-harness alignment to improve tool selection and evidence integration efficiently. Second, curriculum learning organizes shared foundational capabilities and their compositional dependencies in a capability graph for mastery-guided rollouts, periodic review, and targeted remediation. On SpaRW-Eval, SpatialCritic achieves 95.1% verification accuracy, outperforming 320B GLM-5.3-Flash with a 40x smaller 8B backbone and raising average baseline scores from 78.4 to 83.9 on SD3.5-Medium and 83.2 to 86.5 on BAGEL. Project page: https://xiaofeng-tan.github.io/projects/spatialcritic/
Optimizing Agentic Reasoning with Retrieval via Synthetic Semantic Information Gain Reward
Hong Kong JC STEM Lab of Smart City.（香港JC STEM实验室）；City University of Hong Kong.（香港城市大学）；Lingnan University（岭南大学）；Fudan University.（复旦大学）；Huazhong University of Science and Technology.（华中科技大学）
University of British Columbia（不列颠哥伦比亚大学）；Vector Institute（向量研究所）；Kolors Team, Kuaishou Technology（快手团队）；Carnegie Mellon University（卡内基梅隆大学）；University of Waterloo（滑铁卢大学）；Etude AI；Tsinghua University（清华大学）；Georgia Institute of Technology（佐治亚理工学院）
AgentV-RL: Scaling Reward Modeling with Agentic Verifier
College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；Huazhong University of Science and Technology（华中科技大学）；The University of Hong Kong（香港大学）；ByteDance Seed（字节跳动种子）；Institute of Trustworthy Embodied AI, Fudan University（复旦大学可信具身人工智能研究院）；Shanghai Key Laboratory of Multimodal Embodied AI（上海多模态具身人工智能重点实验室）
SQL-ASTRA: Alleviating Sparse Feedback in Agentic SQL via Column-Set Matching and Trajectory Aggregation
Griffith University（格里菲斯大学）；Fudan University（复旦大学）；The University of Hong Kong（香港大学）；Peking University（北京大学）；InFly；Shanghai Innovation Institute（上海创新研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）
HISR: Hindsight Information Modulated Segmental Process Rewards For Multi-turn Agentic Reinforcement Learning
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent Hunyuan（腾讯文脉）；School of Computer Science and Technology, Tianjin University（天津大学计算机科学与技术学院）
Dependency-Aware Reward Shaping for Agentic Reinforcement Learning
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；National University of Singapore（新加坡国立大学）；Zhejiang University（浙江大学）；University of Rochester（罗切斯特大学）
University of Macau（澳门大学）；Qwen Team, Alibaba Inc.（阿里云Qwen团队）；UNSW Sydney（新南威尔士大学悉尼分校）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究所）
CM2: Reinforcement Learning with Checklist Rewards for Multi-Turn and Multi-Step Agentic Tool Use
University of California, Santa Barbara（加州大学圣巴巴拉分校）；Zoom Video Communications（Zoom视频通信公司）；University of Central Florida（中佛罗里达大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）
AgenticRag-R1: Agentic Reinforcement Learning with Stack Memory for Multi-Step Reasoning, Retrieval and Memorizing
National Engineering Research Center of Software Engineering, Peking University（北京大学国家软件工程研究中心）；School of Computer Science, Peking University（北京大学计算机学院）
Towards Agentic Self-Learning LLMs in Search Environment
The Laboratory of Cognition and Decision Intelligence for Complex Systems, Institute of Automation, Chinese Academy of Sciences（认知与决策智能复杂系统实验室，自动化研究所，中国科学院）；School of Artificial Intelligence, University of Chinese Academy of Sciences（人工智能学院，中国科学院大学）；Xiaohongshu Inc（小红书公司）；Meituan Inc（美团公司）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）
VideoTemp-o3: Harmonizing Temporal Grounding and Video Understanding in Agentic Thinking-with-Videos
Shandong University（山东大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Beihang University（北航）；Southern University of Science and Technology（南方科技大学）
Aligning Agents via Planning: A Benchmark for Trajectory-Level Reward Modeling
State Key Laboratory of Novel Software Technology, Nanjing University（南京大学新型软件技术国家重点实验室）；School of Intelligence Science and Technology, Nanjing University（南京大学智能科学与技术学院）；AMAP, Alibaba Group（阿里集团AMAP）
BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search
Institute of Artificial Intelligence, Xiamen University（厦门大学人工智能研究院）；Meituan Inc.（美团公司）；School of Informatics, Xiamen University（厦门大学信息学院）；Westlake University（西湖大学）；Jilin University（吉林大学）；Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan (Xiamen University), Ministry of Culture and Tourism, China（福建省和台湾非物质文化遗产数字化保护与智能处理重点实验室（厦门大学），中华人民共和国文旅部，中国）
Establishing Best Practices for Building Rigorous Agentic Benchmarks
UIUC（伊利诺伊大学）；Stanford University（斯坦福大学）；University of California, Berkeley（加州大学伯克利分校）；Yale University（耶鲁大学）；Princeton University（普林斯顿大学）；MIT（麻省理工学院）；Transluce；ML Commons；Amazon（亚马逊）；UK AI Safety Institute（英国人工智能安全研究所）；University of Oxford（牛津大学）
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Baidu Inc.（百度公司）
Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic deep search. However, its application is often hindered by low \textbf{Reward Density} in deep search scenarios, where agents expend significant exploratory costs for infrequent and often null final rewards. In this paper, we formalize this challenge as the \textbf{Reward Density Optimization} problem, which aims to improve the reward obtained per unit of exploration cost. This paper introduce \textbf{InfoFlow}, a systematic framework that tackles this problem from three aspects. 1) \textbf{Subp…
