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

Official code, models, and dataset for "Evolution Fine-Tuning (EFT): Learning to Discover Across 371 Optimization Tasks"

How Post-Training Shapes Biological Reasoning Models

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