The paper is a great read -- an interesting approach, fun theoretical comparisons, plus benchmarks for optimized algorithms for specific hardware (CPU and GPU,

The paper is a great read -- an interesting approach, fun theoretical comparisons, plus benchmarks for optimized algorithms for specific hardware (CPU and GPU, allowing for differing instruction sets). But it doesn't stop there; from the "Discussion" section: > One important strength of AlphaTensor is its flexibility to support complex stochastic and non-differentiable rewards (from the tensor rank to practical efficiency on specific hardware), in addition to finding algorithms for custom operations in a wide variety of spaces (such as finite fields). We believe this will spur applications of AlphaTensor towards designing algorithms that optimize metrics that we did not consider here, such as numerical stability or energy usage.