I'm getting ready to publish my blog post on the series of NVIDIA Nemotron models (focusing primarily upon post-training) tomorrow morning. Due to providing open weights, data, code, and more, these are some of the most useful tech reports for learning about recent trends in research and minute details of LLM training. I spent a ton of time while on vacation the last few weeks reading through the hundreds of pages of tech reports for these models. It's so interesting to see how trends emerge: - Training becomes more agentic over time. - RL infrastructure evolves for scale. - Varying kinds of RL training (multi-domain versus sequential) are used. - MOPD becomes more common. LLM research moves so quickly, and you can track a lot of the trends through the evolution of these papers / models. Excited to share this with everyone!