# 🎤 I gave a talk at PyTorch Conference NA 2026! > Source: > Published: 2026-10-25 22:02:57+00:00 Still letting this sink in a little. I went to PyTorch Conference NA last week and presented my investigation into a question that had been driving my independent research: **Do Muon and Dion3 retain their convergence advantages over Adam under practical training conditions?** The work took me into mixed-precision training, checkpointing, distributed recovery, and a debugging saga involving learning-rate scheduling, distributed optimizer state, and a silent PyTorch tensor aliasing bug. Along the way, I worked hands-on in OLMo-core, integrated Dion3, ran training experiments, and documented the journey. This project now includes: **The research and experiments:** investigating optimizer behavior under practical training conditions. **The debugging saga:** [Part 1 — The LR Death Spiral](https://medium.com/@jenwei0312/the-lr-death-spiral-escaping-pytorchs-memory-traps-in-distributed-optimizers-da1d724a557f) **The season finale:** [Part 2 — The Anatomy of a Silent Tensor Aliasing Bug](https://medium.com/@jenwei0312/taming-orthogonal-optimizers-at-scale-part-2-the-anatomy-of-a-silent-tensor-aliasing-bug-147431a9c7b0) **The project materials:** [Presentation + OLMo-core PR on Hugging Face](https://huggingface.co/datasets/bird-of-paradise/muon-dion3-pytorchcon-2026) I’m proud that I took this investigation from independent experiments to a conference presentation, and grateful for the opportunity to share it with the PyTorch community. Here’s to more questions about how models learn, more experiments, and more things to debug.