HaloBlock: Faster and Better Decoding for Diffusion Large Language Models

HaloBlock introduces a difficulty‑aware adaptive halo mechanism for diffusion large language models (dLLMs) to address two inference challenges: decoding difficulty dominated by right‑side tokens and boundary contextual discontinuity in block‑wise decoding. By using bidirectional cumulative entropy to assess block difficulty, HaloBlock allocates halo regions on both sides of each block, smoothing cross‑block context propagation and reducing difficulty for hard tokens. Experiments on LLaDA‑8B‑Instruct, Dream‑7B‑Instruct, and LLaDA‑1.5 show consistent accuracy gains and faster convergence on mathematical reasoning and code generation tasks.

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• llms
• diffusion
• benchmarks