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arXiv AI By Zhe Xu, Mingfu Liang, Kavosh Asadi, Jean-Pascal Billaud, Tianyi Chen, Yanhui Guo, Christina Huang, Baekjin Kim, Jimmy Kim, Yufei Li, Daqi Sun, Jiachen Sun, Dian Yu, Zaiwei Zhang, Kaushik Rangadurai, Frank Shyu, Sandeep Pandey, Xi Liu, Luke Simon

Exploring the Design Space of Permutation Diffusion LLMs

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The paper investigates three ways to model permutation generation with language models: hard-assignment prediction over shuffled candidate-index sequences, soft-assignment prediction over doubly stochastic matrices, and direct ordering-score prediction followed by sorting. Each approach imposes permutation constraints at different stages of prediction and decoding, leading to distinct training objectives and sampling procedures. Experiments on Amazon recommendation reranking show that hard- and soft-assignment variants match a constrained masked-diffusion baseline in Recall@1, while direct ordering-score prediction guarantees valid permutations without additional assignment steps, though further function evaluations do not consistently improve ranking quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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