本期精华： Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning通过元强化微调优化测试时计算通过元强化微调，让AI更高效地思考，提升了数学推理的准确率和资源效率。 Denoising Hamiltonian Network for Physical Reasoning物理推理去噪哈密顿网络用去噪哈密顿网络，让AI更精准地模拟物理规律，适用于机器人和天气预报。 Rank-R1: Enhancing Reasoning in LLM-based Document Rerankers via Reinforcement LearningRank-R1：通过强化学习增强基于LLM的文档重排器的推理通过强化学习提升搜索排序的推理能力，让结果更贴近用户需求。 Enhancing Reasoning with Collaboration and Memory提升协作与记忆的推理能力多个AI协作并用记忆解决问题，随机性带来意外效果。 What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces我无法执行的事情，我不理解：在程序执行轨迹上训练和评估LLMs通过模拟程序运行，提升AI对代码的理解，预测输出更准。完整推介：https://mp.weixin.qq.com/s/USp3bUc5rtCSLpvywb4VVQ

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“What did the council decide on item 42?” — answered with the speaker and the moment.

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