Research Questions Whether Arbitrary Token Order in Diffusion Language Models Actually Improves Reasoning
A new arXiv paper challenges the assumption that allowing language models to generate tokens in any order (rather than left-to-right) improves reasoning capabilities. The researchers found that diffusion language models actually perform worse on math and coding tasks when given this flexibility, as they tend to avoid difficult tokens. Their simpler approach, called JustGRPO, achieves strong results (89.1% on GSM8K) while maintaining parallel decoding benefits.
Researchers at Tsinghua University published a preprint arguing that diffusion large language models (dLLMs)—which can generate tokens in arbitrary orders unlike traditional left-to-right models—may be limited rather than enhanced by this flexibility for reasoning tasks. The team observed that dLLMs exploit order flexibility to bypass high-uncertainty tokens critical for solving complex problems, leading to premature collapse of solution coverage. Rather than developing complex reinforcement learning methods to preserve arbitrary ordering, they propose JustGRPO, a minimalist approach that forgoes flexible ordering and applies standard Group Relative Policy Optimization. The method achieves 89.1% accuracy on the GSM8K mathematics benchmark while retaining the parallel decoding advantages of diffusion models. This finding suggests that architectural flexibility does not automatically translate to improved reasoning performance.
What's missing
The paper does not provide comparisons with other recent diffusion language model approaches or discuss performance on non-reasoning tasks where arbitrary ordering might offer advantages. The study's generalizability beyond mathematics and coding tasks remains unclear.
What different sources said
- arXiv cs.AICenter
The Flexibility Trap: Rethinking the Value of Arbitrary Order in Diffusion Language Models
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