We would like the new team to work on some of the following areas: Post-training and reinforcement learning : Techniques used to improve model deployment quality through further training, tuning, RL, and focus on particular downstream tasks; Dataset curation and optimization : Techniques to collect and select high-quality data, which can help models to train or tune more quickly or to higher quality; LLM Pretraining : Techniques to ensure stability and compute-efficiency while pretraining high quality models. May include training dynamics, parameterizations, numerics, or others; Sparsity : Techniques to sparsify models or data that improve training time-to-quality, or optimize inference speed or throughput; Domains : Coding agents, reasoning agents, generative language, image, video.