Decoding Grokking: A Closer Look at Neural Network Learning Dynamics

deep neural networks, the journey from training to generalization isn't always straightforward. Two phenomena, grokking and epoch-wise double descent, challenge the conventional wisdom of training loss and accuracy metrics.

Understanding Grokking and Double Descent

Grokking presents a curious case where training loss plummets rapidly, but test performance only catches up after a significant delay. On the other hand, epoch-wise double descent sees training loss drop consistently while test loss goes through peaks and valleys. These aren't just quirks. they're indicators of complex underlying dynamics.

So, what's really happening? Strip away the marketing and you get two competing processes: representation learning in the encoder and readout calibration in the final classifier. These processes, active throughout training, create the seemingly erratic generalization patterns we observe. Frankly, it's not just about the model's size or its parameter count. The architecture matters more than the parameter count here.

The Role of Representation-Readout Decomposition

Enter the representation-readout decomposition. This framework breaks down the learning dynamics by focusing on how representation learning and readout calibration interact. Here's what the benchmarks actually show: the readout tends to be train-biased before grokking hits, contradicting the lazy-to-rich narrative often touted.

This approach goes beyond mere observation. It provides diagnostic tools to differentiate between spurious and genuine generalization. Take the MNIST grokking example. Delayed or non-monotone generalization there stems from representation degradation and readout misalignment, thanks to unconventional training methods.

Why This Matters

Why should we care? Because understanding these dynamics isn't just academic. It's about making sense of how neural networks learn, potentially guiding more effective training strategies. If we know what's causing these phenomena, we can adjust our approaches to mitigate them. The reality is, a deeper understanding of learning dynamics can lead to more interpretable AI models.

But let's ask the tough question: Are we too fixated on the wrong signals? In an era where AI models keep growing in complexity, should we rethink how we measure success?

Ultimately, the representation-readout decomposition doesn't just explain anomalies. It offers a top-down view of learning dynamics, positioning itself as a valuable tool for interpretability research. The numbers tell a different story, one where understanding outweighs sheer computational power.