A modular, scalable, high-performance training framework for LLMs, VLMs, diffusion, and embodied models.

Revisiting Mid-training in the Era of Reinforcement Learning Scaling

[𝗜𝗖𝗠𝗟 𝟮𝟬𝟮𝟲] Dispersion loss counteracts embedding condensation and improves generalization in small language models

The IDE for mid-training model development

Open catalog of datasets used to train and align LLMs across pretraining, mid-training, and post-training.

How Post-Training Shapes Biological Reasoning Models