PlaidQ: 0.7B continuous diffusion LM distilled to one step for code generation
AlexanderTong7 · x · 2026-09-08
A new arXiv paper introduces PlaidQ, a 0.7B continuous diffusion language model for code generation that repurposes a pretrained autoregressive model as a bidirectional denoiser over continuous token embeddings.
Key results:
- At matched scale, PlaidQ is competitive with discrete diffusion LMs on code generation
- Distillation shifts the quality-compute frontier: a 16-step student hits 31.78 and 40.49 pass@10 on HumanEval and MBPP+, beating its own teacher sampled at 512 steps
- Paired-trajectory distillation pushes to a single denoising step, achieving 7.07 pass@1 on HumanEval with functionally correct programs
The authors argue continuous diffusion offers a viable interface for few-step and one-step language generation. Paper and code are public.
Related event: PlaidQ Distills Continuous Diffusion LMs into Single-Step Code Generation(2 posts)→
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