I just published my overview of the NVIDIA Nemotron series (focusing on post-training)... You can read it here: Among open models / resources, Nemotron is especially useful due to its transparency. Releases often include detailed tech reports, code, training recipes, and even data in some cases. There are many minute details involved in learning how to post-train LLMs at scale, but they are hard to learn without detailed accounts from teams that have actually trained frontier-scale models. NVIDIA Nemotron provides us with a rare opportunity to study these details directly and gain an understanding of how LLM post-training actually works. The overview covers the following, which encompasses the majority of recent Nemotron tech reports: - Llama-Nemotron: - AceReason-Nemotron: - Nemotron-Cascade: - Nemotron-Cascade 2: - Nemotron 3 Nano: - Nemotron 3 Super: - Nemotron 3 Ultra: Through these works, we can see a clear progression in post-training methodologies. Relatively simple pipelines slowly transform to become more agentic, and new components (e.g., MOPD and async / long horizon RL) are introduced over time. Beyond these high-level trends, we can see all of the small details that are useful for getting the most performance out of your model; e.g., curriculum learning, multiple training stages, synthetic data recipes, data filtering / curation / mixing, balancing reasoning efforts, harness exposure, and more. I highly recommend reading this blog (and the associated tech reports) for those who want a realistic view into the practical details of LLM post-training.