SALD: Self-Referenced Advantage Learning for Diffusion Models
Recent work on language-model adaptation has shown that single models can obtain informative training signals by evaluating their behavior in demonstrationor feedback-augmented contexts, with the help of a teacher network, which is driven by the student's learned parameters. Inspired by this internal-reference principle, we investigate how diffusion models can identify self-referenced training signals without external demonstrations or teacher networks. We introduce SALD, a self-referenced training framework that evaluates each image-caption pair at two noise levels using the same model. The easier, lower-noise path is evaluated without gradient tracking to provide a reference, while the ...
| Authors | Aryan Das, Surjo Dey, Koushik Biswas et al. |
|---|---|
| Primary category | cs.CV |
| Published | 1 October 2026 |
| Last updated at source | 7 October 2026 |
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