A high-performance framework for training LLMs, VLMs, diffusion, and embodied models on NVIDIA GPUs and Kunlun XPUs.
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.
Experiments with agentic mid-training and agentic capabilities
Unofficial reproduction of Nemotron-CLIMB: CLustering-based Iterative Data Mixture Bootstrapping for Language Model Pre-training (NeurIPS 2025, arXiv:2504.13161) — search-found mixtures beat uniform baselines +0.014–0.031 STEM at d28; novel finding: selection-mechanism winner's curse; Ascend NPU backend.
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