The challenge
Different models, datasets, budgets, and measurement methods make training algorithms difficult to compare. Measuring algorithmic progress requires a common basis for evaluation.
The work
AlgoPerf puts training algorithms through standardized workloads and evaluation procedures. We help build the open benchmarking infrastructure that makes those comparisons consistent, reproducible, and available for others to inspect.
Why it matters
We make algorithmic progress easier to verify and build on by separating it from changes in hardware, tuning budgets, or experimental setup. Open benchmarks give researchers a common basis for testing ideas, reproducing results, and improving training efficiency.
