为什么 RM 总是学不好推理？揭秘 BT Loss 中被忽视的“距离偏差”
论文标题：WHEN DISTANCE DISTRACTS: REPRESENTATION DIS-TANCE BIAS IN BT-LOSS FOR R
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QwenLong-L1.5：长上下文推理与记忆管理的后训练方案
论文标题：QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memo
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Motif-2-12.7B-Reasoning：RL 训练配方与全栈优化实践指南
论文标题：Motif-2-12.7B-Reasoning: A Practitioner’s Guide to RL Training Recipes
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NVIDIA 提出端到端 RL 编排，8B 模型在 HLE 榜单超越 GPT-5
论文标题：ToolOrchestra: Elevating Intelligence via Efficient Model and Tool Orch
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LayerNorm 真的不可或缺吗？一文读懂超越归一化层的 Derf
论文标题：Stronger Normalization-Free Transformers
论文链接：https://arxiv.org/pdf/2
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UBC & DeepMind 揭示“短上下文主导”现象：80%的生成任务只需最后96个Token
论文标题：SHORT-CONTEXT DOMINANCE: HOW MUCH LOCAL CONTEXT NATURAL LANGUAGE ACTUAL
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谷歌 DeepMind & MIT 发布智能体 Scaling Law
论文标题：Towards a Science of Scaling Agent Systems
论文链接：https://arxiv.org/pdf
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Native Parallel Reasoner: 基于自蒸馏强化学习的原生并行推理框架
论文标题：Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled R
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预训练、中期训练与强化学习在推理模型中的相互作用
论文标题：On the Interplay of Pre-Training, Mid-Training, and RL on Reasoning Lan
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Differential Smoothing——缓解 RL 微调中的分布坍缩并提升 LLM 推理能力
论文标题：Differential Smoothing Mitigates Sharpening and Improves LLM Reasoning
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