The Seed Diffusion model learns structured a priori knowledge of the code through an innovative 'Constraint Order Diffusion' technique to gain a deeper understanding of the logical dependencies of a programming language. This technique enables the model to recognize key programming principles such as 'variables need to be declared before they are used'.
The model adopts a two-phase training strategy: mask-based diffusion training is performed first to cultivate the ability of local context complementation; then edit-based diffusion training is performed to strengthen the global code reasonableness judgment. This training approach empowers the model with excellent code refactoring ability, and maintains the overall consistency when making variable renaming or logic modification.
This answer comes from the articleSeed Diffusion: Validating High-Speed Language Models for Next-Generation ArchitecturesThe
English 
简体中文 
日本語 
Deutsch 
Português do Brasil