Generative AI Paper Reading: Why Larger Models Learn More

Join us for a paper discussion on "Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention" presented by Logan. This paper explains that larger language models can learn rare, complex tasks that smaller models miss because their massive capacity reduces "gradient interference," preventing common tasks from overwriting the features needed to retain rare ones.
https://arxiv.org/pdf/2605.29548

Silicon Valley Generative AI has two meeting formats.

1. Paper Reading - Every second week we meet to discuss machine learning papers. This is a collaboration between Silicon Valley Generative AI and Boulder Data Science.

2. Talks - Once a month we meet to have someone present on a topic related to generative AI. Speakers can range from industry leaders, researchers, startup founders, subject matter experts and those with an interest in a topic and would like to share. Topics vary from technical to business focused. They can be on how the latest in generative models work and how they can be used, applications and adoption of generative AI, demos of projects and startup pitches or legal and ethical topics. The talks are meant to be inclusive and for a more general audience compared to the paper readings.

If you would like to be a speaker please contact:
Matt White