数据库分类 · 12 记录
研究
The papers the modern stack is built on — from the transformer to scaling laws, RLHF and reasoning models.
现场笔记
十几份PDF，撑起了价值万亿美元的基础设施。这些论文记录了最初的思想火花——大多数出乎意料地易读，每条记录都告诉你什么才是关键，无需涉及数学。
从这三个开始
AI
Attention Is All You Need (2017)
八位作者，一种架构，以及此后的一切。
TC
Training Compute-Optimal LLMs — Chinchilla (2022)
改变了每个模型训练方式的那次修正。
DR
DeepSeek-R1 — Reasoning via RL (2025)
推理的开放配方——在竞争对手秘而不宣之际公开发布。
Foundational papers
12
AI
Attention Is All You Need (2017)
The eight Google authors who invented the transformer.
A(
AlexNet (2012)
The ImageNet result that started the deep-learning era — on two gaming GPUs.
CA
Constitutional AI (2022)
Anthropic's method: models critique themselves against written principles.
DR
DeepSeek-R1 — Reasoning via RL (2025)
The open recipe for reasoning models — a Nature cover, from a hedge fund's lab.
DD
Denoising Diffusion Probabilistic Models (2020)
The DDPM paper behind every image and video generator.
F(
FlashAttention (2022)
The kernel trick that made long context affordable.
HA
Highly Accurate Protein Structure Prediction (2021)
AlphaFold 2 in Nature — the result that won a Nobel.
I—
InstructGPT — RLHF at Scale (2022)
How raw language models became assistants people can use.
LM
Language Models are Few-Shot Learners (2020)
The GPT-3 paper: capability from scale, tasks from prompts.
LL
LoRA: Low-Rank Adaptation (2021)
Fine-tuning for the GPU-poor — tiny adapters instead of full weights.
SL
Scaling Laws for Neural Language Models (2020)
Kaplan et al.: loss falls as a power law in compute — the investment thesis, quantified.
TC
Training Compute-Optimal LLMs — Chinchilla (2022)
DeepMind's correction: most models were undertrained on data.