本期精华汇总 [LG] Implicit Search via Discrete Diffusion: A Study on Chess隐式搜索通过离散扩散：关于棋类的探讨通过“隐形思考”技术，AI在国际象棋中实现高效决策，未来可用于更多规划任务。 [IR] Granite Embedding Models[IR]花岗岩嵌入模型IBM开源的高效搜索模型，支持多语言，助力企业优化检索体验。 [LG] Teasing Apart Architecture and Initial Weights as Sources of Inductive Bias in Neural Networks将架构和初始权重作为神经网络的归纳偏置来源分开探讨初始权重优化能缩小网络差距，但结构对新任务的适应性仍关键。 [CL] Distill Not Only Data but Also Rewards: Can Smaller Language Models Surpass Larger Ones?[CL] 不仅蒸馏数据，还要蒸馏奖励：小型语言模型能否超越大型模型？小模型学习大模型的判断力，在数学等任务上实现超越。 [CL] Thinking Slow, Fast: Scaling Inference Compute with Distilled Reasoners[CL] 慢思考，快思考：通过蒸馏推理者扩展推理计算快模型用速度换性能，在资源受限场景下表现更优。完整推介：https://mp.weixin.qq.com/s/cjsoI1nN_zLS370bFQq8vw

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