Phase Change Learning Dynamics and “Grocking”
“Grocking” refers to a sudden leap in the learning capability of a model, where it rapidly goes from barely improving to suddenly achieving high performance. This phenomenon can be seen as a type of phase transition in Learning Dynamics, akin to physical systems undergoing phase changes (e.g., from liquid to gas). The connection to Attractors and Singularities lies in the idea that the learning process may encounter Critical Points in the parameter or state space that trigger these dramatic shifts in performance. At these points, small changes can lead to the system rapidly converging to a new attractor that represents a more effective learning state.
Grokking and Sudden Learning Breakthroughs
Grokking, where a neural network suddenly “gets” a problem after a period of seeming stagnation, might be understood through these concepts. A network at or near Criticality, exploring a connected mode landscape, may suddenly find a pathway through singular points that lead to a significantly better understanding or representation of the problem, resulting in a grokking phenomenon. This breakthrough can be seen as the network transitioning to a deeper, more effective mode of operation that was previously inaccessible or hidden in the landscape.
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