DepCap: Adaptive Block-Wise Parallel Decoding for Efficient Diffusion LM Inference

Researchers introduce DepCap, a training-free framework that optimizes diffusion language model (DLM) inference through adaptive block-wise parallel decoding. The method achieves up to 5.63× speedup by using cross-step signals to determine block boundaries and identifying conflict-free token subsets for safe parallel execution, maintaining quality while significantly accelerating inference.

DepCap addresses a critical efficiency challenge in diffusion language models, which offer theoretical advantages over autoregressive models through parallel decoding potential but suffer from computational bottlenecks during inference. The framework tackles two fundamental decisions in block-wise DLM decoding: determining optimal block boundaries and identifying which tokens can be safely decoded in parallel without conflicts. Traditional approaches rely on fixed schedules or local signals, creating conservative approximations that sacrifice speed for safety. DepCap's innovation lies in leveraging cross-step signals—specifically the influence of previously decoded blocks—to dynamically adjust how far subsequent blocks should extend. This adaptive approach enables more aggressive parallelization while maintaining semantic coherence.

The technical contribution carries significant implications for AI infrastructure and computational efficiency. As language models grow larger and inference demands scale, reducing per-token processing time directly impacts deployment costs, latency-sensitive applications, and energy consumption. A 5.63× speedup without quality degradation represents meaningful progress toward practical DLM deployment. The plug-and-play design ensures compatibility across different DLM architectures and existing optimization strategies like KV-cache, lowering implementation barriers for practitioners.

For the broader AI development ecosystem, this work demonstrates that substantial inference improvements remain achievable through algorithmic refinement rather than architectural changes alone. The information-theoretic analysis supporting the token-level additivity assumption adds theoretical rigor. However, the practical impact depends on DLM adoption rates and competition from optimized autoregressive inference methods. Developers building production systems should monitor whether this efficiency gains translate to meaningful cost reductions in real-world deployments.

• →DepCap achieves up to 5.63× inference speedup for diffusion language models without measurable quality loss through adaptive block partitioning
• →The framework uses cross-step signals to dynamically determine block boundaries instead of relying on fixed schedules or local heuristics
• →Training-free design enables immediate adoption across diverse DLM architectures and remains compatible with existing optimization techniques
• →Information-theoretic analysis validates that cumulative influence across tokens exhibits approximate additivity, supporting the proposed methodology
• →Results span multiple benchmarks including reasoning and coding tasks, demonstrating broad applicability beyond single-domain evaluation