Adaptive Multi-Step Lookahead Decoding for Diffusion Language Models

AdaLook: Adaptive multi-step lookahead decoding for masked diffusion language models

Researchers introduce AdaLook, an adaptive lookahead decoding framework for masked diffusion language models (DLMs) that dynamically determines rollout depth based on candidate-score variance rather than using fixed-depth lookahead. The method also enables branch expansion at informative intermediate states, avoiding unnecessary computation while improving exploration. Experiments across multiple benchmarks show AdaLook achieves a better accuracy-to-decoding-steps trade-off than existing one-step lookahead methods. The work addresses a known limitation of parallel text generation via DLMs, which are a non-autoregressive alternative to standard transformer decoding.