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About this paper
Zhihui Xie, Jiacheng Ye, Lin Zheng, Jiahui Gao, Jingwei Dong, Zirui Wu, Xueliang Zhao, Shansan Gong and 3 more
A collaboration between The University of Hong Kong and Huawei Noah’s Ark Lab introduces Dream-Coder 7B, the first open-source discrete diffusion language model for code, achieving performance on par with state-of-the-art autoregressive models. This model exhibits emergent adaptive generation patterns, including sketch-first scaffolding and interleaved reasoning, for various coding tasks.
Key insights
- Discrete diffusion language models can match and even surpass the performance of state-of-the-art autoregressive models for code generation tasks when appropriately trained.
- Diffusion training naturally leads to emergent, adaptive generation patterns like sketch-first scaffolding, left-to-right completion, and interleaved reasoning, adapting to task complexity.
- Specialized post-training techniques, including supervised fine-tuning that mitigates padding pathologies and reinforcement learning with verifiable rewards, are critical for competitive performance in diffusion models for code.
Summary by alphaXiv.
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