爱可可 AI 前沿推介(10.12)

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LG - 机器学习 CV - 计算机视觉 CL - 计算与语言 AS - 音频与语音 RO - 机器人 1、[CL] LLM Cascade with Multi-Objective Optimal Consideration 2、[LG] Rewarding Progress：Scaling Automated Process Verifiers for LLM Reasoning 3、[LG] Can Transformers Reason Logically? A Study in SAT Solving 4、[CL] Upcycling Large Language Models into Mixture of Experts 5、[LG] Features are fate：a theory of transfer learning in high-dimensional regression 摘要：考虑多目标优化的LLM级联策略、用奖励进展扩展LLM推理的自动化流程验证器、Transformer能进行逻辑推理吗、将大型语言模型升级为专家混合模型、高维回归迁移学习特征空间的重叠程度最关键 1、[CL] LLM Cascade with Multi-Objective Optimal Consideration K Zhang, L Peng, C Wang, A Go, X Liu [Google AIR] 考虑多目标优化的LLM级联策略 要点: 大型语言模型的高部署成本: 文章强调了部署大型语言模型（LLM）的巨大成本，尤其 ………………………………