Beyond the Trial-and-Error Loop: Hybrid Projection and Automated Tuning for Distributed Training.

arXiv:2610.19423v1 Announce Type: new Abstract: Configuring distributed LLM training across hundreds of GPUs requires jointly optimizing tensor, pipeline, expert, context, and data parallelism alongside micro-batch size, recomputation, sharding, and overlap, a