llms

Source-backed Pagish topic assembled from the current AI intelligence feed.

ResearchSep 2, 2026watch

FP4 training research points to the next fight over AI efficiency

Efficiency research is becoming one of the highest-leverage parts of AI progress. Work on FP4 block scaling for stable language-model pretraining points at the pressure to train capable models with less memory, less power, and better hardware utilization.

Why it matters: For the market, efficiency work compounds. Better training formats can lower the cost of future models, improve utilization of new accelerators, and make infrastructure investments stretch further.

ResearchAug 31, 2026watch

Post-training is starting to look like maintenance work, not magic

A useful AI research signal this week is the move to describe LLM post-training as industrial maintenance. That framing is important because many model improvements depend less on mystery and more on cleaning, shaping, measuring, and repairing the data systems around the model.

Why it matters: For builders, this makes model quality a process question. The teams that improve fastest will likely be the ones with the best feedback loops, data hygiene, and evaluation discipline, not only the biggest base model.