Seed Diffusion has demonstrated superior performance in several authoritative code review benchmarks:
- In benchmarks such as LiveCodeBench, Bigcode Bench, Mbpp and Human Eval, the performance is comparable to autoregressive models of the same size
- In the code editing task, the diffusion model outperforms the traditional autoregressive model due to its global perspective property
- Particularly adept at handling tasks that require structured reasoning, such as variable renaming, function refactoring, etc., and can ensure global consistency of modifications
This is mainly due to its two-stage training mechanism: first learning code-completion ability through mask diffusion training, and then improving global rationality through edit diffusion training.
This answer comes from the articleSeed Diffusion: Validating High-Speed Language Models for Next-Generation ArchitecturesThe
May not be reproduced without permission:AI productivity tools " How well does Seed Diffusion perform in code generation tasks?
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