Diffusion models have demonstrated strong generative performance; however, generated samples often fail to fully align with human intent. This paper studies an efficient test-time scaling method for sampling from regions with higher human-aligned reward values. Existing methods for computing the expected future reward (EFR) face important limitations: backward rollout incurs prohibitively high sampling costs, while Tweedie-based approaches, including Sequential Monte Carlo and gradient guidance, suffer from bias and inherent sampling issues. We show that the EFR at any $\mathbf{x}_t$ can be computed using only marginal samples from a pre-trained diffusion model, enabling closed-form reward guidance without neural backpropagation. To further improve efficiency, we introduce a few-step lookahead sampling and an accurate solver that guides particles toward high-reward lookahead samples. We refer to this sampling scheme as LiDAR sampling. LiDAR achieves the same GenEval performance as the latest gradient guidance method for SDXL with a 9.5x speedup. We release the code at https://github.com/aailab-kaist/Diffusion-LiDAR-Sampling.
Alignment of Diffusion Model and Flow Matching for Text-to-Image Generation
Department of Statistics, University of California, Los Angeles（加州大学洛杉矶分校统计学系）；Department of Industrial and Systems Engineering, University of Minneasota（明尼苏达大学工业与系统工程系）；School of Data Science, The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）数据科学学院）
Towards a unified framework for guided diffusion models
Department of Statistics, the Chinese University of Hong Kong, Hong Kong（香港中文大学统计学系）；Department of Statistics and Data Science, the Wharton School, University of Pennsylvania（宾夕法尼亚大学沃顿商学院统计学与数据科学系）
Flow-Direct: Feedback-Efficient and Reusable Guidance for Flow Models via Non-Parametric Guidance Field
College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院，新加坡）；Centre for Frontier AI Research, Agency for Science, Technology and Research, Singapore（前沿人工智能研究中心，新加坡科技研究局）；Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore（高性能计算研究所，新加坡科技研究局）
Lookahead Path Likelihood Optimization for Diffusion LLMs
Institute for Artificial Intelligence, Peking University（人工智能研究院，北京大学）；School of Computing, National University of Singapore（计算学院，新加坡国立大学）；School of Intelligence Science and Technology, Peking University（智能科学与技术学院，北京大学）
Co-Evolution of Policy and Internal Reward for Language Agents
McGill University（麦吉尔大学）；McMaster University（麦克马斯特大学）；The University of Hong Kong（香港大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Peking University（北京大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；DeepWisdom（深度智慧）；Université de Montréal（蒙特利尔大学）；Mila（米拉研究所）
Reinforcement Learning with Curriculum-inspired Adaptive Direct Policy Guidance for Truck Dispatching
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）
SocraticPO: Policy Optimization via Interactive Guidance
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（认知智能国家重点实验室，中国科学技术大学）；iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd（iFLYTEK中央中国AI研究院，iFLYTEK公司）
Learning Diverse Policies with Soft Self-Generated Guidance
School of Mathematical Sciences, Beihang University（北京航空航天大学数学科学学院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Zhongguancun Laboratory（中关村实验室）
Hybrid-AIRL: Enhancing Inverse Reinforcement Learning with Supervised Expert Guidance
Artificial Intelligence Lab — Vrije Universiteit Brussel（自由大学布鲁塞尔人工智能实验室）；College of Science and Engineering — University of Galway（盖尔威大学科学与工程学院）；Family Medicine and Population Health — University of Antwerp（安特卫普大学家庭医学与人口健康）
Dual-Granularity Contrastive Reward via Generated Episodic Guidance for Efficient Embodied RL
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（多模态人工智能系统国家重点实验室，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）
Variance-aware Reward Modeling with Anchor Guidance
Department of Data Science and Artificial Intelligence, Hong Kong Polytechnic University（香港理工大学数据科学与人工智能系）；School of Statistics and Data Science, Shanghai University of Finance and Economics（上海财经大学统计与数据科学学院）
Eliminating Inductive Bias in Reward Models with Information-Theoretic Guidance
Qwen Large Model Application Team, Alibaba（阿里巴巴大模型应用团队）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Research Institute of Big Data（深圳大数据研究院）
Policy Optimization with Smooth Guidance Learned from State-Only Demonstrations
School of Mathematical Sciences, Beihang University（北京航空航天大学数学科学学院）；Institute of Artificial Intelligence, Beihang University（北京航空航天大学人工智能研究院）；Zhongguancun Laboratory（中关村实验室）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京高精尖创新中心）
KGRGRL: A User's Permission Reasoning Method Based on Knowledge Graph Reward Guidance Reinforcement Learning
Command and Control Engineering College, Army Engineering University of PLA（中国人民解放军陆军工程大学指挥控制工程学院）；troop of PLA（中国人民解放军31436部队）；Academy of Military Science（军事科学院）
