A diffusion code model cuts generation from 512 steps to 16
Most diffusion language models trade token-by-token decoding for dozens or hundreds of refinement passes. PlaidQ, a 0.7B continuous diffusion model, distills a 512-step teacher into 16 steps while reaching 31.78 pass@10 on HumanEval and 40.49 on MBPP+, then produces functionally correct code in a single step at 7.07 pass@1 on HumanEval. The result makes diffusion look less like an alternative sampler and more like a practical compression target for code generation.