Measuring Optimal Transport in Transformer Depth
The episode discusses 'Measuring Optimal Transport in Transformer Depth,' analyzing how large models' internal movements evolve during training. Hosts discuss that while models generally move toward optimal efficiency, specific gaps—like at the Pythia-160m exit—show areas of inefficiency. The findings suggest scale and better measurement tools are key to building more efficient AI architectures.