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IDRF: Inverse-Distilled Reward Fine-tuning of Masked Discrete Diffusion Models
Masked discrete diffusion models offer a promising alternative to autoregressive generation, but iterative sampling can be costly, and intractable sequence likelihoods complicate reward fine-tuning. We introduce IDRF, a framework for reward fine-tuning of few-step masked discrete diffusion generator
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