Bagian basis data · 12 rekaman
Riset
The papers the modern stack is built on — from the transformer to scaling laws, RLHF and reasoning models.
Catatan lapangan
Selusin PDF menopang infrastruktur senilai satu triliun dolar. Inilah makalah-makalah tempat gagasan-gagasan itu pertama kali muncul — sebagian besar mengejutkan mudah dibaca, dan setiap entri di sini menjelaskan hal-hal yang penting tanpa perlu matematika.
Mulai dengan tiga ini
AI
Attention Is All You Need (2017)
Delapan penulis, satu arsitektur, dan segalanya sejak saat itu.
TC
Training Compute-Optimal LLMs — Chinchilla (2022)
Koreksi yang mengubah cara setiap model dilatih.
DR
DeepSeek-R1 — Reasoning via RL (2025)
Resep terbuka untuk penalaran — diterbitkan saat para pesaing menyimpan milik mereka.
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.