UCT, a state-of-the art algorithm for Monte Carlo tree search (MCTS) in games and Markov decision processes, is based on UCB1, a sampling policy for the Multi-armed Bandit problem (MAB) that minimizes the cumulative regret. However, search differs from MAB in that in MCTS it is usually only the final "arm pull" (the actual move selection) that collects a reward, rather than all "arm pulls". In this paper, an MCTS sampling policy based on Value of Information (VOI) estimates of rollouts is suggested. Empirical evaluation of the policy and comparison to UCB1 and UCT is performed on random MAB instances as well as on Computer Go.
Sycophancy Mitigation Through Reinforcement Learning with Uncertainty-Aware Adaptive Reasoning Trajectories
University of California, Davis（加州大学戴维斯分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Virginia Tech（弗吉尼亚理工大学）；Meta AI；Adobe Research（Adobe研究）
TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Department of Computer and Information Science, University of Pennsylvania（计算机与信息科学系，宾夕法尼亚大学）；School of Mathematics, Georgia Institute of Technology（数学学院，佐治亚理工学院）；Department of Bioengineering, University of Pennsylvania（生物工程系，宾夕法尼亚大学）
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
State Key Laboratory for Novel Software Technology at Nanjing University（南京大学新型软件技术国家重点实验室）；School of Computer Science, Nanjing University（南京大学计算机科学学院）；Suzhou Laboratory（苏州实验室）
CAHS-Attack: CLIP-Aware Heuristic Search Attack Method for Stable Diffusion
Beijing University of Posts and Telecommunications（北京邮电大学）；China Mobile Ltd, Department of Cyber and Information Security Management（中国移动有限公司，网络安全与信息安全管理部）；Aspire Information Technology (Beijing) Company Limited（aspire信息技术（北京）有限公司）
