Large language models are increasingly deployed in multi-turn interactive settings where users or environments can iteratively provide lightweight feedback. Unfortunately, optimizing such behavior presents a sharp dilemma in practice: online reinforcement learning is able to effectively address multi-turn dynamics but is prohibitively expensive due to the cost of generating full correction trajectories at every update, whereas offline supervised fine-tuning (SFT) is efficient but suffers from distribution shift and behavioral collapse. To this end, we novelly propose DRIFT (Decoupled Rollouts and Importance-Weighted Fine-Tuning), a framework that operationalizes the theoretical insight that the KL-regularized RL objective is equivalent to importance-weighted supervised learning. DRIFT decouples rollout from optimization by sampling offline interaction trajectories from a fixed reference policy, deriving return-based importance weights, and optimizing the policy via weighted SFT on the resulting dataset. Empirically, we demonstrate that DRIFT matches or exceeds the performance of multi-turn reinforcement learning baselines while maintaining the training efficiency and simplicity of standard supervised fine-tuning. Code is available at https://github.com/2020-qqtcg/DRIFT.
Efficient Multi-turn RL for GUI Agents via Decoupled Training and Adaptive Data Curation
Beijing Institute of Technology（北京理工大学）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；DataCanvas；Beijing University of Posts and Telecommunications（北京邮电大学）；Shenzhen MSU-BIT University（深圳MSU-BIT大学）
GD$^2$PO: Mitigating Multi-Reward Conflicts via Group-Dynamic reward-Decoupled Policy Optimization
Qwen Large Model Application Team, Alibaba（阿里巴巴通义千问大模型应用团队）；Renmin University of China（中国人民大学）；Peking University（北京大学）；ETH Zürich（苏黎世联邦理工学院）；University of Zurich（苏黎世大学）；The Chinese University of Hong Kong（香港中文大学）
RASFT: Rollout-Adaptive Supervised Fine-Tuning for Reasoning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；New Jersey Institute of Technology（新泽西理工学院）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Rollout-Level Advantage-Prioritized Experience Replay for GRPO
Department of Electrical and Computer Engineering, Seoul National University（首尔国立大学电子与计算机工程系）；Interdisciplinary Program in AI, Seoul National University（首尔国立大学人工智能跨学科项目）；AIIS, ASRI, INMC, and ISRC, Seoul National University（首尔国立大学人工智能研究所、人工智能研究机构、智能网络与计算中心及人工智能科学研究中心）
First Order Model-Based RL through Decoupled Backpropagation
Machines in Motion Laboratory, New York University, USA（纽约大学运动机器实验室）；LAAS-CNRS, Université de Toulouse, CNRS, Toulouse, France（图卢兹大学LAAS-CNRS）；Artificial and Natural Intelligence Toulouse Institute, Toulouse, France（图卢兹人工智能与自然智能研究所）
Learning with a Single Rollout via Pass@k Policy Optimization
University of Alberta（阿尔伯塔大学）；BIGAI（北京通用人工智能研究院）；University of Toronto（多伦多大学）；McGill University, Mila（麦吉尔大学，米拉）；Nvidia Research（英伟达研究院）；Amii（阿尔伯塔机器智能研究所）；CIFAR AI Chair（CIFAR人工智能教席）
Slow-Fast Policy Optimization: Reposition-Before-Update for LLM Reasoning
Georgia Institute of Technology（佐治亚理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Manchester（曼彻斯特大学）；NVIDIA；Independent（独立）；Emory University（埃默里大学）
GIRL: Generative Imagination Reinforcement Learning via Information-Theoretic Hallucination Control
Department of Computer Science and Engineering, Visvesvaraya Technological University (VTU), Belagavi, India（维斯瓦拉亚科技大学计算机科学与工程系，贝拉加维，印度）；Aliens on Earth (AoE) Autonomous Research Group, Belagavi, India（地球外星人自主研究组，贝拉加维，印度）
Revisiting On-Policy Distillation: Empirical Failure Modes and Simple Fixes
State Key Laboratory of Multimodal Artificial Intelligence Systems, CASIA（多模态人工智能系统国家重点实验室，中科院自动化所）；School of Artificial Intelligence, UCAS（中国科学技术大学人工智能学院）；Fudan University（复旦大学）；Independent Researcher（独立研究者）
PriorZero: Bridging Language Priors and World Models for Decision Making
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；The Chinese University of Hong Kong MMLab（香港中文大学MMLab）
Decoupled Structure-Feature Alignment via Alternating Optimization for Graph Learning
College of Artificial Intelligence, Shaoxing Institute of Technology（绍兴文理学院人工智能学院）；School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）
Overcoming Catastrophic Forgetting in Visual Continual Learning with Reinforcement Fine-Tuning
The University of Hong Kong（香港大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；Hong Kong Generative AI Research and Development Center（香港生成式人工智能研究与开发中心）
