We refine SVG generation using online reinforcement learning with image reconstruction, semantic, and code-level rewards, boosting accuracy, efficiency, and interpretability.

This paper introduces an unsupervised method that disentangles interpretable latent concepts in language model activations that mediate behavior, assuming that sparse changes in these concepts can induce changes in model behavior.