[2601.15165] The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models

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Computer Science > Computation and Language

arXiv:2601.15165 (cs)

[Submitted on 21 Jan 2026 (v1), last revised 8 Jun 2026 (this version, v4)]

Title:The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models

Authors:Zanlin Ni, Shenzhi Wang, Yang Yue, Tianyu Yu, Weilin Zhao, Yeguo Hua, Tianyi Chen, Jun Song, Cheng Yu, Bo Zheng, Gao Huang<br>View a PDF of the paper titled The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models, by Zanlin Ni and 10 other authors

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Abstract:Diffusion Large Language Models (dLLMs) break the rigid left-to-right constraint of traditional LLMs, enabling token generation in arbitrary orders. Intuitively, this flexibility implies a solution space that strictly supersets the fixed autoregressive trajectory, theoretically unlocking superior reasoning potential. However, in this paper, we find that for general reasoning tasks (e.g., mathematics and coding), arbitrary order generation may in fact limit the reasoning potential of dLLMs. We observe that dLLMs tend to exploit this order flexibility to bypass high-uncertainty tokens that are crucial for exploration, which can lead to a premature collapse of solution coverage. This observation motivates a rethink of RL approaches for dLLMs, where considerable complexities, such as handling combinatorial trajectories and intractable likelihoods, are often devoted to preserving this flexibility. We show that effective reasoning can be elicited by simply forgoing arbitrary order and applying standard Group Relative Policy Optimization (GRPO) instead. Our approach, JustGRPO, is minimalist yet surprisingly effective (e.g., 89.1% accuracy on GSM8K) while fully retaining the parallel decoding ability of dLLMs. Project page: this https URL

Comments:<br>Code and pre-trained models: this https URL

Subjects:

Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)

Cite as:<br>arXiv:2601.15165 [cs.CL]

(or<br>arXiv:2601.15165v4 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2601.15165

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arXiv-issued DOI via DataCite

Submission history<br>From: Zanlin Ni [view email]<br>[v1]<br>Wed, 21 Jan 2026 16:41:58 UTC (2,006 KB)

[v2]<br>Mon, 26 Jan 2026 08:29:32 UTC (2,272 KB)

[v3]<br>Thu, 19 Mar 2026 02:53:39 UTC (2,274 KB)

[v4]<br>Mon, 8 Jun 2026 15:43:52 UTC (2,081 KB)

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