Market regime shifts induce distribution shifts that can degrade the performance of portfolio rebalancing policies. We propose macro-conditioned scenario-context rollout (SCR) that generates plausible next-day multivariate return scenarios under stress events. However, doing so faces new challenges, as history will never tell what would have happened differently. As a result, incorporating scenario-based rewards from rollouts introduces a reward--transition mismatch in temporal-difference learning, destabilizing RL critic training.
We analyze this inconsistency and show it leads to a mixed evaluation target. Guided by this analysis, we construct a counterfactual next state using the rollout-implied continuations and augment the critic agent's bootstrap target. Doing so stabilizes the learning and provides a viable bias-variance tradeoff.
In out-of-sample evaluations across 31 distinct universes of U.S. equity and ETF portfolios, our method improves Sharpe ratio by up to 76% and reduces maximum drawdown by up to 53% compared with classic and RL-based portfolio rebalancing baselines.
SERA: Scale-Equalized Rollout Allocation for Maximum Likelihood Reinforcement Learning
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Nanyang Technological University（南洋理工大学）
Contextual Rollout Bandits for Reinforcement Learning with Verifiable Rewards
School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；School of Artificial Intelligence, Beihang University（北京航空航天大学人工智能学院）；Huawei（华为）
EchoRL: Reinforcement Learning via Rollout Echoing
Munich Center for Machine Learning（慕尼黑机器学习中心）；Huawei Heisenberg Research Center（华为海森堡研究所以）；University of Arizona（亚利桑那大学）；College of Computing（计算学院）；Data Science, Nanyang Technological University, Singapore（数据科学，南洋理工大学，新加坡）；MemAgents Lab（MemAgents实验室）
Reinforcement Learning for Portfolio Optimization with a Financial Goal and Defined Time Horizons
AIMS South Africa /Stellenbosch university, South Africa（AIMS南非 /斯托滕博斯大学，南非）；University of South Africa, South Africa（南非大学，南非）；Michigan State University, USA（密歇根州立大学，美国）
SAMP-HDRL: Segmented Allocation with Momentum-Adjusted Utility for Multi-agent Portfolio Management via Hierarchical Deep Reinforcement Learning
School of AI and Advanced Computing, XJTLU Entrepreneur College (Taicang), Xi’an Jiaotong-Liverpool University（人工智能与先进计算学院，XJTLU创业学院（太仓），西安交通大学利物浦大学）；Department of Management Science and Engineering, School of Economics and Management, Xinjiang University（管理科学与工程系，经济管理学院，新疆大学）
Act Only When It Pays: Efficient Reinforcement Learning for LLM Reasoning via Selective Rollouts
Carnegie Mellon University（卡内基梅隆大学）；Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Meta AI
GRPODropout: Less is More for Online Reinforcement Learning Rollouts
Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）计算与智能学院）；Beijing Zhongguancun Academy（北京中关村学院）；XinzhuAI（芯智科技）
Deep Reinforcement Learning for Investor-Specific Portfolio Optimization: A Volatility-Guided Asset Selection Approach
School of Mathematical and Statistical Sciences, Indian Institute of Technology Mandi（印度理工学院曼迪数学与统计科学学院）；Department of Mathematics, Indian Institute of Technology Guwahati（印度理工学院古瓦哈提数学系）
RollVerify: Bridging Efficiency and Accuracy in Long-Tail Rollout Reinforcement Learning
Shanghai Jiao Tong University（上海交通大学）；Central South University（中南大学）；SenseTime Research（商汤科技研究院）；The Chinese University of Hong Kong（香港中文大学）；Beihang University（北京航空航天大学）
DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
School of Computer Science \& Beijing Key Laboratory of Software；Hardware Cooperative Artificial Intelligence Systems, Peking University, Beijing, China；School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China；Institute of Computational Social Science, Peking University (Qingdao), Qingdao, China
Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts
School of Information, Renmin University of China（中国人民大学信息学院）；Key Laboratory of Data Engineering and Knowledge Engineering（数据工程与知识工程重点实验室）；Ant Group（蚂蚁集团）
APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
Advanced Micro Devices, Inc. (AMD)（先进微器件公司）；Carnegie Mellon University (CMU)（卡内基梅隆大学）；LMSYS Org（LMSYS组织）；University of California, Los Angeles (UCLA)（加州大学洛杉矶分校）
