PairFlow: Closed-Form Source-Target Coupling for Few-Step Generation in Discrete Flow Models
arXiv:2512.20063v3 Announce Type: replace Abstract: We introduce $\texttt{PairFlow}$, a lightweight preprocessing step for training Discrete Flow Models (DFMs) to achieve few-step sampling without requiring a pretrained teacher. DFMs have recently emerged as a new class of generative models for discrete data, offering strong performance. However, they suffer from slow sampling due to their iterative nature. Existing acceleration methods largely depend on finetuning, which introduces substantial additional training overhead. $\texttt{PairFlow}$ addresses this issue with a lightweight preprocess
The continuous drive for more efficient and faster generative AI models necessitates innovation in sampling methods, such as those for Discrete Flow Models.
This development could significantly reduce the computational cost and time associated with high-performance discrete generative AI, making advanced models more accessible and practical for real-world applications.
The introduction of PairFlow allows for few-step sampling in Discrete Flow Models without extensive fine-tuning, directly addressing a key bottleneck in model deployment.
- · AI researchers
- · Developers using discrete generative models
- · Cloud computing providers (potentially reduced load per inference)
- · Prior acceleration methods requiring extensive fine-tuning
- · High-latency generative AI applications
Faster sampling enables wider adoption of Discrete Flow Models in production environments.
Reduced computational requirements could lower the barrier to entry for developing and deploying sophisticated AI applications, fostering innovation.
More efficient generative AI could accelerate progress in fields reliant on synthetic data generation, from drug discovery to content creation.
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Read at arXiv cs.LG