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.
This is the most efficient high-level introduction to pre-training I’ve found. The breakdowns on cost, scale’s impact on loss, and scale versus data quality are good directional starting points for the core questions a pre-training engineer faces.
Building off of the last paragraph, there is some literature out there that suggests that scaling pre-training compute improves 'reflection' capabilities and reasoning efficiency at inference. [Rethinking Reflection in Pre-Training (Essential AI)] Even though the results are on sub-frontier scale models, it provides potential directional signal that scaling and optimizing pre-training directly subsidizes the cost of inference-time reasoning. I'm curious whether pretraining will also dictate the asymptotic ceiling of reasoning quality post-training methods like RL can extract.
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).
This is the most efficient high-level introduction to pre-training I’ve found. The breakdowns on cost, scale’s impact on loss, and scale versus data quality are good directional starting points for the core questions a pre-training engineer faces.
Building off of the last paragraph, there is some literature out there that suggests that scaling pre-training compute improves 'reflection' capabilities and reasoning efficiency at inference. [Rethinking Reflection in Pre-Training (Essential AI)] Even though the results are on sub-frontier scale models, it provides potential directional signal that scaling and optimizing pre-training directly subsidizes the cost of inference-time reasoning. I'm curious whether pretraining will also dictate the asymptotic ceiling of reasoning quality post-training methods like RL can extract.