AlphaTensor explained – Motivation, method, and assessment

AlphaTensor (from DeepMind) discovers mathematical algorithms with Reinforcement Learning. While much of the attention has been on AlphaTensor's results, its successes more truly lie in the novel approach it uses rather than the results themselves.

In DeepMind's AlphaTensor Explained, I outline the details of the model and its key ideas. An objective assessment of the results obtained by AlphaTensor for matrix multiplication algorithms is also given at the end.

You highlight that AlphaTensor can be used to optimize for a variety of distance metrics (speed, memory, numerical stability, energy usage, etc.). Would the distance metrics be what changed if you were using AlphaTensor to find better matrix multiplication algorithms for sparse matrices? What would this distance metric look like, if so