Muse Spark AI Model Development for Personal Superintelligence Scaling

Scaling AxesTo build personal superintelligence, our model's capabilities should scale predictably and efficiently.

Below, we share how we study and track Muse Spark's scaling properties along three axes: pretraining, reinforcement learning, and test-time reasoning.

The pretraining phase is where Muse Spark acquires its core multimodal understanding, reasoning, and coding abilities — the foundation that reinforcement learning and test-time compute build upon.

Over the last nine months, we rebuilt our pretraining stack with improvements to model architecture, optimization, and data curation.