DeepSeek-R1 v2 发布：新增技术细节与训练流程全解读
论文标题：DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforceme
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WAIT, WAIT, WAIT... WHY DO REASONING MODELS LOOP?
论文标题：WAIT, WAIT, WAIT... WHY DO REASONING MODELS LOOP?
论文链接：https://arxiv.
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陈丹琦团队新作：负样本强化在 LLM 推理中的有效性机制
论文标题：The Surprising Effectiveness of Negative Reinforcement in LLM Reasoning
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深度解读 Recursive Language Models：利用递归与环境交互突破 LLM 上下文限制
论文标题：RECURSIVE LANGUAGE MODELS
论文链接：https://arxiv.org/pdf/2512.24601v1
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论大语言模型强化学习训练中的 KL 正则化
论文标题：A COMEDY OF ESTIMATORS: ON KL REGULARIZATION IN RL TRAINING OF LLMS
论
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阿里新作 Let It Flow: Agentic Crafting on Rock and Roll 深度解读
论文标题：Building the ROME Model within an Open Agentic Learning Ecosystem
论文链
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DeepSeek 新作 mHC：为何要把超连接约束在流形上？
论文标题：mHC: Manifold-Constrained Hyper-Connections
论文链接：https://arxiv.org/pd
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字节 Seed 新作：通过辅助损失实现 MoE 专家与路由器的紧密耦合 (ERC Loss)
祝大家新年快乐～
论文标题：Coupling Experts and Routers in Mixture-of-Experts via an Auxi
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Bottom-up Policy Optimization: 自下而上的策略优化——语言模型内部潜藏的子策略
论文标题：Bottom-up Policy Optimization: Your Language Model Policy Secretly Cont
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Google DeepMind 新作：自回归模型中的涌现时间抽象实现了分层强化学习
论文标题：Emergent temporal abstractions in autoregressive models enable hierarch
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