pretraining
pretraining at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Explore how a third pretraining axis transforms end-to-end data generation.”, “Discover how AI predicts your blood sugar from meals and insulin data”, and “Demystifying the math behind the algorithms powering modern LLMs”. Most language models are trained along two axes: data and compute. A model that predicts your blood sugar for the next two hours by reading past glucose, carbs, and insulin, while also conditioning on future meal and insulin plans, is a serious step toward practical AI in daily health. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every pretraining story on Beyond Market Intelligence, newest first.
Explore how a third pretraining axis transforms end-to-end data generation.
Most language models are trained along two axes: data and compute. Gladstone and colleagues argue for a third: the modeling objective itself. Their work on explorative modeling suggests that how we frame the learning task is as important as the data we feed it. This isn't a tweak; it's a different approach to end-to-end generation. For a deeper look at how such models navigate structure, our guide on paragraph structure in token space is a useful next stop.
Discover how AI predicts your blood sugar from meals and insulin data
A model that predicts your blood sugar for the next two hours by reading past glucose, carbs, and insulin, while also conditioning on future meal and insulin plans, is a serious step toward practical AI in daily health. The work here is notable for its honesty: it requires announced inputs, and the author openly notes the limitations. That transparency matters. The inclusion of a nano variant with under 40K parameters shows respect for accessibility.
Demystifying the math behind the algorithms powering modern LLMs
If you've been following the technical reports from Kimi, DeepSeek, Qwen, and GLM, you've likely noticed how much on-policy distillation and GRPO-style algorithms now power the frontier. That's why this deep dive is so timely. It unpacks the maths and code behind these methods, then connects them back to pretraining and supervised fine-tuning. It's a practical resource for anyone wanting to move beyond the hype and actually understand how modern LLMs are trained.