CORA-Diff: Confidence-Oriented Residual Acceptance for Efficient Diffusion Language Model Inference
This episode analyzes 'CORA-Diff,' a method for accelerating diffusion language model inference. The hosts discuss how CORA-Diff achieves massive speedups by identifying and accepting tokens that have stabilized early in the decoding process, using only the model’s internal confidence and persistence signals without requiring any retraining or architectural changes.