DAPO：大规模开源 LLM 强化学习系统
一篇关于 DAPO 的中文阅读笔记：它把 Clip-Higher、动态采样、token-level loss 与 overlong reward shaping 组合成可复现的大规模 LLM 强化学习配方。
DAPO: An Open-Source LLM Reinforcement Learning System at Scale
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RL Training
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LLM Training
A detailed technical review of DAPO, an open-source large-scale reinforcement learning recipe for reasoning LLMs using Clip-Higher, dynamic sampling, token-level loss, and overlong reward shaping.
A detailed technical review of DAPO, an open-source large-scale reinforcement learning recipe for reasoning LLMs using Clip-Higher, dynamic sampling, token-level loss, and overlong reward shaping.
MASPO：面向 LLM 多智能体系统的联合提示词优化
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AI Agents
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Prompt Optimization
一篇关于 MASPO 的中文阅读笔记：它用 local、lookahead 与 global 三类信号联合优化 LLM 多智能体系统中的角色提示词。
一篇关于 MASPO 的中文阅读笔记：它用 local、lookahead 与 global 三类信号联合优化 LLM 多智能体系统中的角色提示词。
MASPO: Joint Prompt Optimization for LLM-based Multi-Agent Systems
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AI Agents
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Prompt Optimization
A detailed technical review of MASPO, a joint prompt optimization method for multi-agent LLM systems that balances local, downstream, and global rewards.
A detailed technical review of MASPO, a joint prompt optimization method for multi-agent LLM systems that balances local, downstream, and global rewards.
Tutti：让基于 SSD 的 KV Cache 真正适用于长上下文 LLM Serving
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ML Systems
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LLM Inference
一篇关于 Tutti 的中文阅读笔记：它从 GPU-native KV cache object store、GPU io_uring 与 slack-aware scheduling 出发，让 SSD-backed KV cache 更适合长上下文 LLM serving。
一篇关于 Tutti 的中文阅读笔记：它从 GPU-native KV cache object store、GPU io_uring 与 slack-aware scheduling 出发，让 SSD-backed KV cache 更适合长上下文 LLM serving。
Tutti: Making SSD-Backed KV Cache Practical for Long-Context LLM Serving
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ML Systems
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LLM Inference
A detailed technical review of Tutti, a GPU-centric SSD-backed KV cache system that makes long-context LLM serving cache reuse practical.
A detailed technical review of Tutti, a GPU-centric SSD-backed KV cache system that makes long-context LLM serving cache reuse practical.
Queueing Stability for LLM Inference with KV Cache Memory Constraints
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ML Systems
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LLM Inference
A detailed technical review of a queueing-theoretic framework for predicting LLM inference stability under KV cache memory constraints.
A detailed technical review of a queueing-theoretic framework for predicting LLM inference stability under KV cache memory constraints.
Swift-SVD: Activation-Aware Low-Rank Compression for LLM Weights and KV Cache
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SVD Acceleration
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Efficient ML
A detailed technical review of Swift-SVD, an activation-aware low-rank compression method for LLM weights and KV cache that uses output covariance eigendecomposition to avoid expensive generalized SVD.
A detailed technical review of Swift-SVD, an activation-aware low-rank compression method for LLM weights and KV cache that uses output covariance eigendecomposition to avoid expensive generalized SVD.
Piper: Efficient Large-Scale MoE Training via Resource Modeling and Pipelined Hybrid Parallelism
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ML Systems
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Training Infrastructure
A detailed technical review of Piper, a resource-model-driven system for large-scale MoE training with pipelined hybrid parallelism, HALO hierarchical all-to-all, and topology-aware expert placement.
A detailed technical review of Piper, a resource-model-driven system for large-scale MoE training with pipelined hybrid parallelism, HALO hierarchical all-to-all, and topology-aware expert placement.
Low-Rank Optimization Trajectories for LLM RLVR Acceleration: A Technical Review of NExt
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LLM Training
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Optimization
A detailed technical review of NExt, a method that models low-rank optimization trajectories to accelerate reinforcement learning with verifiable rewards for large language models.
A detailed technical review of NExt, a method that models low-rank optimization trajectories to accelerate reinforcement learning with verifiable rewards for large language models.
FEPLB Technical Review: Nearly Free MoE Load Balancing with the NVLink Copy Engine
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A detailed technical review of FEPLB, a system that uses Hopper NVLink Copy Engines to perform fine-grained MoE load balancing with little interference to normal expert-parallel training.
A detailed technical review of FEPLB, a system that uses Hopper NVLink Copy Engines to perform fine-grained MoE load balancing with little interference to normal expert-parallel training.
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond — Technical Review
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Paper Review
A technical review of agentic world modeling, covering capability levels, governing-law regimes, evaluation, and why decision-centric world models matter for LLM agents.
Author: Zhongzhu Zhou
Paper: Chu et al., 2026. arXiv:2604.22748 [cs.AI]
Date: April 27, 2026
Direction: Monday, April 27 — Agent/LLM Quality Generation
Pages: 10
Executive Summary
As AI systems evolve from text generators to goal-achieving agents that interact with complex environments, predicting environment dynamics has become the central bottleneck. This comprehensive survey paper provides a unified framework for understanding world models—internal representations that agents use to anticipate consequences of their actions and plan accordingly.
The paper introduces a elegant "levels × laws" taxonomy:
- Three capability levels (L1 Predictor → L2 Simulator → L3 Evolver) define what a world model can do
- Four governing-law regimes (physical, digital, social, scientific) define the constraints it must satisfy
By synthesizing over 400 papers across model-based RL, video generation, web/GUI agents, multi-agent simulation, and AI-driven science, the authors reveal a fragmented landscape where "world model" means different things to different communities. Their framework provides the common language needed to align these communities.