Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting
GoldiMask is introduced, which selects tokens to reveal as context by approximately maximizing a submodular objective and weights the remaining targets according to how they benefit from the selected context and their remaining learning potential.