Fine-Tuning Diffusion Language Models with Context Selection and Target Weighting
Advancing precision in discrete diffusion models through strategic token masking and refined weight optimization.
Supervised fine-tuning of discrete diffusion language models masks some response tokens and trains the model to recover their original values from the visible context. The masking pattern therefore determines both the context available to the model and the tokens it learns to pre...
ArXiv CS.AI
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This research optimizes how diffusion models learn by strategically masking tokens, bridging a critical gap between generative speed and linguistic accuracy. By refining target weighting, the framework allows for more robust training that significantly improves text coherence in discrete language models.
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