The AI industry spent years chasing parameter counts. Bigger models, more layers, wider hidden dimensions. Then a series of results shattered the assumption that size is destiny. Small models trained on carefully curated data…
Every piece of data fed into a model is an action that shapes the model’s future behavior. The consequences aren’t random. They’re structurally determined by the nature of the input. Garbage in, garbage out…
Standard DPO data has two elements: a chosen response and a rejected response. The model learns to prefer one over the other. Simple. Effective. Limited. The Triangle of Correction adds a third element that…
Most AI training data emphasizes breadth: comprehensive factual content across domains. Models memorize vast amounts of information because that’s what pre-training optimizes for. But there’s a deeper capability that most datasets neglect entirely: the…
The pitch is seductive. Running out of training data? Just have AI generate more. Use your existing model to create synthetic datasets, then train the next model on those. Problem solved. Except the evidence…
There’s a problem nobody in the AI industry likes to talk about publicly. We’re running out of training data. Not hypothetically. Not in some distant future. Now. The internet is big, but it’s not…
You’d think experts would make the best annotators. They understand the domain deeply. They can evaluate quality with precision. They know what good looks like. But in practice, domain experts consistently produce worse training…
Most datasets train one capability at a time. Reasoning datasets train reasoning. Conversation datasets train conversation. Code datasets train code. This produces models that are good at specific tasks and mediocre at integrating capabilities….
Curation is the most undervalued skill in AI. Anyone can scrape the internet and dump it into a training pipeline. Building a dataset that actually produces a good model requires judgment, patience, and taste….
The AI industry obsesses over architecture. Transformers vs. state space models. Dense vs. mixture-of-experts. Billions vs. trillions of parameters. These choices matter. But they matter less than the data. Two models with identical architecture…