293 posts in total. Keep on posting.

2026

• 06-23 中

  Critique-GRPO：用自然语言批评反馈突破强化学习训练瓶颈

  Critique-GRPO 将自然语言批评反馈引入在线强化学习循环，解决纯数值奖励训练的三大结构性瓶颈——性能平台期、无效的自发自我反思与持续性失败——在 AIME 2024 等高难度推理基准上实现最高 +26.7% 的 Pass@1 提升。

• 06-22 EN

  MRAgent: Why Memory Should Be Reconstructed, Not Retrieved

  MRAgent replaces passive top-k retrieval with active, multi-step graph traversal over a Cue-Tag-Content associative memory, achieving up to 23% improvement on long-horizon conversational benchmarks while using 5x fewer tokens than competing methods.

• 06-22 中

  MRAgent：记忆应该被重建，而不是被检索

  MRAgent 用主动多步图遍历取代被动 top-k 检索，在长对话记忆基准上最高提升 23%，同时将 token 消耗降低至竞品的 1/5。

• 06-21 EN

  Tutti: GPU-Centric SSD-Backed KV Cache That Finally Makes SSDs Practical for Long-Context LLM Serving

  Tutti eliminates CPU intervention from the KV cache I/O path by introducing GPU io_uring and slack-aware scheduling, achieving DRAM-like efficiency from NVMe SSDs at 100x lower cost per GB.

• 06-21 中

  Tutti 阅读笔记：GPU 原生 SSD KV 缓存，让 NVMe 固态硬盘真正可用于长上下文大模型推理

  Tutti 通过 GPU io_uring 机制消除 KV 缓存 I/O 路径中的 CPU 介入，配合时隙感知调度器，使 NVMe SSD 达到接近 DRAM 的推理性能，同时将每 GB 存储成本降低约 100 倍。

• 06-19 EN

  LASER: How Throwing Away 99% of a Weight Matrix Can Make LLMs Smarter

  LASER shows the counterintuitive result that selectively replacing weight matrices with heavily truncated SVD approximations — keeping as little as 1% of the rank — can boost LLM reasoning accuracy by up to 27 percentage points without any fine-tuning.

• 06-19 中

  LASER：丢掉 99% 的矩阵秩，LLM 推理准确率反而提高了 27%

  LASER 表明：对 Transformer 特定层的权重矩阵做极度激进的 SVD 截断——只保留 1% 的秩——能在不做任何微调的情况下让 LLM 的推理准确率提升最高 27 个百分点。

• 06-18 EN

  LUMEN: Load-Aware Coordinated Failure Recovery for Distributed LLM Serving

  LUMEN treats GPU worker failure recovery in LLM serving as a load-aware coordination problem across three decision points, cutting mean TTFT by 44% and recovery time by 50% over stop-and-restart.

• 06-18 中

  LUMEN：面向分布式大模型推理的负载感知协同故障恢复

  LUMEN 把 LLM 服务集群中的 GPU worker 故障恢复，重新定义为一个负载感知的协同调度问题，在四种 worker 的 Qwen3-32B 实验中，将平均 TTFT 降低 44%、恢复时间缩短 50%。

• 06-17 EN

  OScaR: Occam's Razor for Extreme KV Cache Quantization

  OScaR proposes Canalized Rotation and Omni-Token Scaling to fix Token Norm Imbalance in INT2 KV cache quantization, achieving near-lossless accuracy with 5.3× memory reduction and 4.1× throughput gain.

• 06-17 中

  OScaR：极端 KV 缓存量化的奥卡姆剃刀

  OScaR 用通道旋转与全方位令牌缩放解决逐通道量化中的令牌范数不平衡问题，在 INT2 下实现近无损精度并带来 5.3× 内存压缩和 4.1× 吞吐提升。

• 06-16 EN

  Back to Basics: Revisiting REINFORCE Style Optimization for RLHF (RLOO)

  RLOO shows that PPO is unnecessarily complex for RLHF — a simple REINFORCE Leave-One-Out estimator using k completions per prompt outperforms PPO, DPO, and RAFT with fewer models and no critic network.