Topic: Data scaling

Microsoft researcher Alexia Jolicoeur-Martineau explains the Tiny Recursive Model and the reasoning behind her work on small networks that repeatedly refine an internal state and proposed answer. The conversation examines truncated gradients, puzzle-solving benchmarks and the differences from autoregressive language generation. It also considers molecular modelling, limits of data scaling and which research directions she would prioritize. Hosted by Ravid Shwartz Ziv and Allen Roush. Watch the full conversation on the publisher’s YouTube channel.

Sasha Rush surveys research on increasing language-model capability through computation at inference time. The lecture places test-time scaling alongside parameter and data scaling, examines model self-improvement and discusses implications for the design of reasoning models and open-source research efforts.