Title: Path Planning for Diffusion Language Model Sampling
Primary area: generative models
Keywords: Protein Generation, RNA Generation, large language model, diffusion language models, discrete diffusion models

Abstract: Any order generation of discrete data using masked diffusion models (MDMs) offers a compelling alternative to traditional autoregressive models, especially in domains that lack a natural causal ordering of data. However, current popular masked diffusion models depart from their successful continuous diffusion model counterparts with simplified masked inference wherein unmasked tokens cannot be iteratively refined- even if there is a mistake. In this paper, we extract the full power of MDMs by introducing a novel inference sampling strategy termed Path Planning (P2) that decomposes each generation step into two sub-stages: planning and denoising. Under P2, the planner at every step selects appropriate tokens that are marked to be updated, which can then be sampled using the denoiser. We demonstrate that P2 generalizes all existing sampling strategies for MDMs and critically enhances generative quality through the new capability of 
refining and updating existing unmasked tokens. We theoretically prove that P2 establishes a (new) expanded evidence lower bound (ELBO) on the log marginal likelihood of data. We instantiate P2 with a family of planners including: 1.) Self-Planning, 2.) BERT-Planning, and 3.) Trained-Planning with a learned planner leading to SOTA generative performance for MDMs on a suite of domains. Specifically, solely using P2 inference, we observe relative improvements of $22\%$ in protein sequence foldability, $8\%$ in RNA sequence pLDDT, $4\%$ in math reasoning, $68\%$ in story generation (ROUGE score), and $33\%$ in code generation for the challenging pass@1 metric.

TL;DR: We introduce Path Planning, a diffusion language model sampling method that improves performance with planed decoding order.