CUDA-l2: Surpassing cuBLAS performance for matrix multiplication through RL

They claim the algorithm "discovered" the new techniques, but the methods described in section 5 do not seem all that novel to me. It smells like it could be "laundering" the literature [1] and reshuffling existing techniques. This is not inherently a bad thing, but I would hope that if it is borrowing existing techniques, the appropriate citation would eventually make it into this paper.

[1]: https://www.argmin.net/p/lore-laundering-machines

The chart confused me because I expected to see performance numbers of CUDA-L2 compared to the others, but instead it shows a chart showing the speedup percentage of CUDA-L2 over the others. In some sense, the bar chart effectively inverts the performance of torch.matmul and cuBLAS with how much percentage it shows. 0% on the bar chart would only mean equal performance.

Am I reading this wrong, or does this only support FP16 inputs, and compares its performance against an FP32 solver?

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