I’m running an RLHF pipeline and need a sharp, data-driven review of the training code to understand exactly where time and compute are being lost. The sole focus is algorithm efficiency during the model-training stage; everything else in the codebase is stable for now. By surfacing and fixing the slow spots we should see cleaner gradients, faster convergence, and ultimately better decision-making accuracy from the model. What I’ll hand over • A self-contained repository (Python, PyTorch or other language) with reward model, PPO loop, and evaluation scripts • A brief outline of the current hardware limits and expected throughput What I expect back • A profiled breakdown highlighting hotspots in the training loop, dataloaders, and reward computation • Concrete, code-level recommendations or patches that reduce wall-clock training time without harming results • A short note explaining any trade-offs you introduce so I can reproduce and benchmark I already run basic line-profiler and torch-autograd checks, so I’m looking for deeper insights—vectorised ops, smarter batching, async data movement, or architectural tweaks I may have missed. Feel free to use tools like PyTorch Profiler, nvprof, or your preferred optimisers as long as the final instructions remain reproducible in a standard CUDA environment. If that sounds straightforward, let me know your availability and how you’d approach the first pass; I’m ready to share the repo right away. https://docs.google.com/document/d/1DkZx1WgC6DpLHtiJytm1E0QowykAoPOAyL4R83ufqJY/edit?pli=1&tab=t.0 https://docs.google.com/document/d/1evk6AFVWT_2_RxLyoyWmQr9OSyOgpWQW4Aun-N924s0/edit?tab=t.0#heading=h.q1drz87ywotm