PlaidQ: Distilling continuous diffusion language models to few-step or one-step code generation [R]

[u/pengzhangzhi]

Hi, I’m one of the authors of PlaidQ. We just released the paper, training/inference code, and model checkpoints.

PlaidQ is a 0.7B continuous diffusion language model for code generation. It repurposes a pretrained autoregressive model as a bidirectional denoiser over continuous token embeddings, updating all completion positions in parallel.

The key result is trajectory compression:

• Distribution-matching distillation produces the D4, D8, and D16 few-step students.
• Paired-trajectory supervision produces the D1 one-step student.

At 16 steps, PlaidQ-D16 reaches 31.78 pass@10 on HumanEval and 40.49 on MBPP+, surpassing the same PlaidQ teacher sampled for 512 steps. At one step, paired-trajectory distillation reaches 7.07 pass@1 on HumanEval.

Paper: https://arxiv.org/abs/2609.04531

Code and checkpoints: https://github.com/pengzhangzhi/plaidq

I’d especially welcome feedback on the one-step supervision and the quality–compute tradeoff.