Beyond Market Intelligence/pretraining

pretraining

pretraining on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on pretraining in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around pretraining, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

Machine Learning

"Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation", Gladstone et al. 2026 [R]

Gladstone et al.'s forthcoming paper, "Explorative Modeling: Unlocking a Third Pretraining Axis and End-to-End Generation," introduces a significant advancement in AI model development. This work proposes a novel pretraining strategy, expanding beyond existing approaches to enable more intuitive and capable generative models. The research promises to reshape how we approach data-driven AI, offering a future-focused path toward more adaptable and efficient systems. For a broader perspective on the current landscape of machine learning research, explore our discussion on regaining coherence in the field.

Machine Learning

I have trained a model to predict my blood sugar [P]

A novel AI model for blood sugar prediction has been released, offering a future-focused approach to diabetes management. This encoder-only transformer, leveraging a BERT-style architecture, accurately forecasts blood glucose levels up to two hours ahead by analyzing past and future data (glucose, carbs, insulin), conditioned on announced meals and boluses. Four model sizes exist, ranging from a compact nano version (<40K parameters) to a 17-million-parameter large model. As discussed in "Conference Reviews: Asking Too Much?

Machine Learning

Deep Dive on RL and OPD for Training LLMs [D]

Recent advancements in large language model (LLM) training, exemplified by models like Kimi and Qwen, increasingly leverage policy distillation and reinforcement learning from human feedback (RLHF) techniques. To demystify these powerful methods, we’ve published a deep dive exploring the underlying mathematics and code—connecting these algorithms to pretraining and supervised fine-tuning. Discover how RL and OPD are shaping the future of LLMs. Explore the full explanation here: [https://youtu.be/MaZWafi4gYY?is=8jLkAp_Fe86abUVP](https://youtu.be/MaZWafi4gYY?is=8j