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arXiv · 2603.19146

D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding

Jonathan Lys·Vincent Gripon·Axel Marmoret·Lukas Mauch·Fabien Cardinaux·Ghouthi Boukli Hacene·Bastien Pasdeloup
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Abstract

Discrete diffusion models are promising alternatives to autoregressive approaches for text generation, yet their decoding methods remain under-studied. Standard autoregressive search procedures, such as beam search, do not directly apply to iterative denoising, where hypotheses are complete intermediate sequences rather than left-to-right prefixes. Furthermore, existing diffusion decoding procedures only provide limited control over the diversity and coverage of retained hypotheses. In this work, we introduce D5P4, a beam-style decoding method tailored to discrete diffusion models, which casts intermediate beam selection as MAP inference under a partitioned Determinantal Point Process. This yields a model-internal batch objective that balances quality and diversity without external verifiers. Experiments on open-ended generation, question answering, and mathematical reasoning show that D5P4 improves diversity and pass@$k$ coverage while matching or surpassing baseline quality and fidelity

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Jonathan Lys, Vincent Gripon, Axel Marmoret, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene, Bastien Pasdeloup. 2026-03-19. D5P4: Partition Determinantal Point Process for Diversity in Parallel Discrete Diffusion Decoding. https://arxiv.org/abs/2603.19146

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