Continuous Representations Improve On Discrete Ones for SVG Generation

Current autoregressive SVG generation models represent coordinates as a sequence of discrete tokens, even though those values describe continuous, geometric positions. We propose an alternative, *continuous* approach that encodes coordinates through a value encoder and predicts them via regression through a value decoder, while retaining ordinary token prediction for drawing commands and markup. In evaluations, our approach achieves higher reconstruction quality than all evaluated discrete baselines. Moreover, data-scaling experiments with 5%, 25%, and 100% of the training data show a persistent advantage over discrete approaches at every training-set size, showing that our approach learns faster and peaks higher than discrete baselines. We investigate this advantage by analyzing *numerical representations*, *coordinate generalization*, and *coordinate precision*. For numerical representations, we find that our embeddings vary more smoothly and are more linearly decodable than discrete representations. For coordinate generalization, continuous predictions remain stable on held-out coordinates, while discrete predictions degrade. For coordinate precision, continuous predictions yield lower teacher-forced coordinate error than their discrete counterparts on fractional targets. Together, these results favor a more natural division of labor: continuous values for geometry and discrete tokens for drawing structure.