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Arxiv
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Inference-Time Scaling of Diffusion Language Models with Particle Gibbs Sampling
- Discrete diffusion models have become powerful for language modeling, competing with auto-regressive models in training-time scaling.
- A new approach using particle Gibbs sampling for inference-time scaling in discrete diffusion models is introduced.
- The particle Gibbs sampling algorithm refines diffusion trajectories iteratively using Sequential Monte Carlo to improve text generation.
- Empirical results show that this new approach outperforms prior inference-time strategies in reward-guided text generation tasks.
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