新闻
Learning When Diffusion Language Models Should Regenerate
arXiv cs.AI · 发布于 · 阅读约3分钟
30秒读懂
- 发生了什么
- DiVR, a verifier that learns when diffusion language models should regenerate text blocks, improves reasoning accuracy from 35.3% to 43.4% on benchmarks.
- 为何重要
- Engineers building or optimizing diffusion-based language models for reasoning tasks need better decoding strategies beyond greedy generation.
- 注意
- DiVR costs 1.9 to 3.5 times more than greedy decoding, and effectiveness depends on training the verifier on model-specific trajectories.
- language model
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