Shownotes Transcript
本期的 15 篇论文如下：
[00:22] 🤖 Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-Play（Voila：用于实时自主交互和语音角色扮演的语音-语言基础模型）
[01:09] 🤔 RM-R1: Reward Modeling as Reasoning（RM-R1：将奖励建模视为推理）
[01:52] 🧠 Grokking in the Wild: Data Augmentation for Real-World Multi-Hop Reasoning with Transformers（野外Grokking：用于Transformer真实世界多跳推理的数据增强）
[02:32] 🧮 FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models（FormalMATH：大规模语言模型的形式化数学推理基准）
[03:17] ✂ ReplaceMe: Network Simplification via Layer Pruning and Linear Transformations（ReplaceMe：基于层剪枝和线性变换的网络简化）
[03:59] 🧠 Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL（通过拒绝采样和强化学习中的梯度方差最小化优化思维链推理器）
[04:39] 🚀 Practical Efficiency of Muon for Pretraining（Muon在预训练中的实际效率）
[05:18] ⚙ A Survey on Inference Engines for Large Language Models: Perspectives on Optimization and Efficiency（大语言模型推理引擎综述：优化与效率的视角）
[06:01] 🤖 R1-Reward: Training Multimodal Reward Model Through Stable Reinforcement Learning（R1-奖励：通过稳定强化学习训练多模态奖励模型）
[06:44] 🤔 Think on your Feet: Adaptive Thinking via Reinforcement Learning for Social Agents（随机应变：基于强化学习的社交智能体自适应思考）
[07:24] 🤖 SkillMimic-V2: Learning Robust and Generalizable Interaction Skills from Sparse and Noisy Demonstrations（SkillMimic-V2：从稀疏和嘈杂的示范中学习鲁棒且可泛化的交互技能）
[08:03] 🤖 Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning（基于强化学习的LLM自主推理与工具集成）
[08:50] 🖼 SuperEdit: Rectifying and Facilitating Supervision for Instruction-Based Image Editing（SuperEdit：修正并促进基于指令的图像编辑的监督）
[09:30] 🧮 Low-Precision Training of Large Language Models: Methods, Challenges, and Opportunities（大语言模型低精度训练：方法、挑战与机遇）
[10:11] 🎨 Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction（Ming-Lite-Uni：自然多模态交互统一架构的进展）
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