Having now read the paper, the research area is interesting because optimizations in optimal path finding have applications in robotics, gaming, and reasoning (what the ultimate intention of this paper is).
The research team identified ways to tokenized path finding algorithms for two tasks maze solving and sokoban (a game where a crate has to be pushed to goal) and then trained a model on the execution traces of these algorithms.
The insight this provided was that the "searchformer" model was about 26% faster than the traditional methods. If that is applied to Route-planning, Robotics, and Game Development, it could have tangible performance benefits.
IMHO, it is not a wild breakthrough but an interesting solution to a real-world problem.
The research team identified ways to tokenized path finding algorithms for two tasks maze solving and sokoban (a game where a crate has to be pushed to goal) and then trained a model on the execution traces of these algorithms.
The insight this provided was that the "searchformer" model was about 26% faster than the traditional methods. If that is applied to Route-planning, Robotics, and Game Development, it could have tangible performance benefits.
IMHO, it is not a wild breakthrough but an interesting solution to a real-world problem.