Predict-then-Diffuse: Adaptive Response Length for Compute-Budgeted Inference in Diffusion LLMs

Researchers propose Predict-then-Diffuse, a framework that optimizes diffusion-based large language models by predicting required response length before generation, reducing computational waste from padding tokens and re-computation overhead while maintaining output quality across multiple datasets.

Diffusion-based Large Language Models represent a fundamental shift in how generative AI processes language by enabling fully parallel token generation, contrasting sharply with the sequential approach of traditional autoregressive models. This architectural innovation promises significant throughput gains and improved GPU utilization—critical advantages for scaling production systems. However, this parallelism introduces a critical constraint: the model must commit to a fixed response length before generation begins, forcing a difficult trade-off between computational efficiency and output quality.

The core problem stems from this architectural rigidity. When response length predictions are too generous, the model wastes computation on semantically meaningless padding tokens. Conversely, undersized predictions trigger costly re-computation cycles that introduce unpredictable latency spikes—a particularly acute problem for latency-sensitive applications. The Predict-then-Diffuse framework addresses this through an Adaptive Response Length Predictor auxiliary model that estimates optimal output length per query, combined with a data-driven safety mechanism that accounts for prediction uncertainty.

For AI infrastructure developers and organizations deploying large-scale language models, this optimization directly impacts operational costs and system reliability. Reducing FLOP requirements translates to lower inference costs, faster response times, and more stable performance characteristics. The framework's model-agnostic design enables adoption across different D-LLM architectures without requiring retraining. Testing across multiple datasets demonstrates robustness even against skewed data distributions—a practical validation that addresses real-world deployment challenges.

The significance lies not in breakthrough capability gains but in elegant efficiency improvements that compound across billions of inferences. As production AI systems scale, such optimizations determine competitive advantage through reduced infrastructure costs and improved resource utilization.

• →Predict-then-Diffuse reduces computational waste in diffusion LLMs by predicting optimal response length before generation.
• →The framework trades negligible padding overhead for robustness against undersized predictions and costly re-computation cycles.
• →Experimental results show significant FLOP reduction compared to default D-LLM inference and heuristic-based baselines.
• →The model-agnostic design enables adoption across different diffusion LLM architectures without architectural modifications.
• →Framework demonstrates robustness across multiple datasets and skewed data distributions relevant to production deployments.