Topic Overviews
Follow how problems, methods, and trade-offs evolve within a research area.
What do these statuses mean?
Status describes reading readiness within the current scope. Ready to Read articles may still receive updates.
Ready to Read A coherent treatment of the current scope, ready for end-to-end reading.
In Progress Substantial content is available; sections, derivations, or validation are still being developed.
Outline An outline, source collection, or early notes.
- Topic Overviews In ProgressLong Context：问题、解决方法、主流方案与现状
- Topic Overviews In ProgressOverview of LLM Inference
梳理大语言模型推理中的采样策略、Prefill 与 Decode 性能分析、KV cache、Continuous batching、并行推理、Speculative decoding 与 Multi-token prediction，并讨论生成结果的可复现性。
- Topic Overviews In ProgressOverview of Visual Foundation Models
- Topic Overviews In ProgressOverview of Policy Optimization Methods
- Topic Overviews In ProgressOverview of position encoding
- Topic Overviews In ProgressScaling law
Publish‑ready workflow that lets you focus on ideas, not infrastructure
- Topic Overviews In ProgressMoE tutorial
本 blog 详细介绍了 MoE 模型的一些关键设计与相关实验结果，为 MoE 模型的学习提供基础。
- Topic Overviews In ProgressOverview of optimizers for LLMs
Overview of optimizers for LLMs
- Topic Overviews In ProgressOverview of Attention Mechanism
A hands-on guide to understanding and implementing the Attention mechanism in deep learning models.
- Topic Overviews In ProgressOverview of Flash Attention series
作者提出了 flashattention, 一个通过降低 multi head attention 内存访问开销来提高 attention 计算效率的方法
- Topic Overviews In ProgressOverview of Multimodal Large Language Models
- Topic Overviews In ProgressOverview of OPD
- Topic Overviews In ProgressOverview of RLHF
Overview of RLHF
- Topic Overviews OutlineOverview of unified MLLM
Overview of unified MLLM
- Topic Overviews OutlineOverview of RLVR
Overview of RLVR
- Topic Overviews OutlineLow Precision