NeurIPS

IFlowNets: Extending Generative Samplers to Learn Strategies in Incomplete Information Games

IFlowNets：将生成采样器扩展至不完美信息博弈中学习策略

Conor M. Artman, Nicholas Di, Scott Perkins

AI总结 该研究将AFlowNets扩展为适用于不完美信息博弈的IFlowNets，解决了原有约束无法得到有效密度与训练目标的问题，在标准博弈环境中性能与速度优于或相当于OSMCCFR等方法。

Comments Accepted at the NeurIPS 2025 Workshop on Dynamics at the Frontiers of Optimization, Sampling, and Games

Shrinking the Generation-Verification Gap with Weak Verifiers

缩小生成-验证差距的弱验证器

Jon Saad-Falcon, E. Kelly Buchanan, Mayee F. Chen, Tzu-Heng Huang, Brendan McLaughlin, Tanvir Bhathal, Shang Zhu, Ben Athiwaratkun, Frederic Sala, Scott Linderman, Azalia Mirhoseini, Christopher Ré

机构 * Stanford University（斯坦福大学） ； University of Wisconsin-Madison（威斯康星大学麦迪逊分校） ； Together AI 纠错

AI总结 Weaver通过结合多个弱验证器，有效缩小生成-验证差距，提升验证性能至接近理想验证器水平。

Comments Annual Conference on Neural Information Processing Systems (NeurIPS) 2025