Modern AI models are built on the idea that increasing data, model size, and compute drives major performance gains. As these features become harder to scale, what does that imply for the path ahead?
Outstanding deep dive. The Sardana et al. framing around inference-optimized scaling is super clarifying, makes the Llama-3 training choices feel way less arbitrary. Also didnt realize Schaeffer's work basically debunked emergence as just measurement artifacts, thats kinda huge for predicting capabilities.
Outstanding deep dive. The Sardana et al. framing around inference-optimized scaling is super clarifying, makes the Llama-3 training choices feel way less arbitrary. Also didnt realize Schaeffer's work basically debunked emergence as just measurement artifacts, thats kinda huge for predicting capabilities.
Baffles me how this piece hasn't got more attention. Really high-quality and palatable for non-technical folks (me).