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-ref
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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-ref
This research introduces a novel self-referenced training framework for diffusion models, potentially reducing the reliance on external data or complex teacher networks. For ML engineers, this could lead to more efficient and scalable training methodologies for generative models, especially in scenarios where high-quality labeled data or external feedback is scarce.