[R] I am looking for good research papers on compute optimization during model training, ways to reduce FLOPs, memory usage, and training time without hurting convergence.

A viral research request is crowdsourcing the secret playbook for compute-optimal AI training. Experts are hunting for papers on techniques like mixed precision, gradient checkpointing, and ZeRO to drastically reduce FLOPs, memory usage, and training time without hurting model performance. The goal is to find practical methods that work on real multi-GPU setups, potentially slashing the massive costs and weeks-long timelines associated with training state-of-the-art models.