aiqbench analysis
The paper proposes SDDC, a method that has a diffusion language model first write a contract describing how a program splits into units and how those units depend on each other, then implement the units in parallel. The reported gains are a pass@1 increase of up to 31.1 points and 7.5 to 13.7x faster decoding on two 7B backbones, but these are self-reported results on five benchmarks, so their generality is untested. The main takeaway for readers is a concrete technique for managing the correctness-versus-speed trade-off in parallel decoding for code.
Self-Decomposing Diffusion for Code Generation
arXiv:2610.27600v1 Announce Type: new Abstract: Diffusion language models (DLMs) are emerging as a promising paradigm for code generation, offering the potential for fast, parallel decoding. However, generating complex programs remains challenging, as their interdependent components must maintain consistent interfaces, data flows, and shared assumptions. Aggressive parallel decoding can compromise these dependencies and undermine program correctness, while conservative decoding sacrifices efficiency. To address this trade-off, we propose planning program decomposition before implementation to coordinate interdependent units during parallel generation. We introduce Self-Decomposing Diffusion for Code (SDDC), a novel framework that decomposes complex programs and implements their units in parallel, entirely through diffusion. SDDC first generates a contract specifying the program's decomposition and inter-unit dependencies, then uses it to guide coordinated parallel implementation. Across five benchmarks and two 7B diffusion backbones, SDDC substantially improves the accuracy--latency trade-off, raising pass@1 by up to 31.1 percentage points while decoding 7.5--13.7$\times$ faster than the base models, and outperforming a fine-tuning baseline by up to 23.5 points with lower latency.
Abstract:Diffusion language models (DLMs) are emerging as a promising paradigm for code generation, offering the potential for fast, parallel decoding. However, generating complex programs remains challenging, as their interdependent components must maintain consistent interfaces, data flows, and shared assumptions. Aggressive parallel decoding can compromise these dependencies and undermine program correctness, while conservative decoding sacrifices efficiency. To address this trade-off, we propose planning program decomposition before implementation to coordinate interdependent units during parallel generation. We introduce Self-Decomposing Diffusion for Code (SDDC), a novel framework that decomposes complex programs and implements their units in parallel, entirely through diffusion. SDDC first generates a contract specifying the program's decomposition and inter-unit dependencies, then uses it to guide coordinated parallel implementation. Across five benchmarks and two 7B diffusion backbones, SDDC substantially improves the accuracy--latency trade-off, raising pass@1 by up to 31.1 percentage points while decoding 7.5--13.7$\times$ faster than the base models, and outperforming a fine-tuning baseline by up to 23.5 points with lower latency.
Computation and Language (cs.CL)
arXiv:2610.27600 [cs.CL]
(or arXiv:2610.27600v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.27600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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