Inference, Diffusion, World Models, and More – YC Paper Club [video]
This video from YC Paper Club covers key AI research papers on inference, diffusion models, and world models, discussing their methodologies and implications for the field.
Background
This is a public recording of Y Combinator's internal "Paper Club" — a recurring meeting where YC staff and partners discuss recent AI research papers. YC is the startup accelerator behind companies like Airbnb, Stripe, and OpenAI. The discussion covers: (1) **inference scaling laws** — the idea that spending more computation during inference (answering time) can dramatically improve model performance, with o1 and Kimi k1.5 as examples; (2) **diffusion models** for text generation (e.g., generating text by denoising from randomness, unlike the standard autoregressive token-by-token approach); (3) **world models** — neural-network-based simulators that learn an internal model of how environments work (e.g., GameNGen, which runs a playable Doom-like game inside a neural net). The audience includes YC founders and technologists deciding where to invest or build.