连续扩散语言模型的分布匹配蒸馏方法
Distribution Matching Distillation for Continuous Diffusion Language Models
OpenAI新研究提出两种蒸馏方法，大幅降低扩散模型计算成本，4次评估即可生成1024个高质量token。
研究人员提出两种分布匹配蒸馏方法，用于减少连续扩散语言模型的网络评估次数。Simplex-DMD在OpenWebText文本上，仅用4次网络评估即可达到45.6的困惑度，比基线模型减少49%。Reinforce-DMD在256次网络评估下，困惑度降至14.9，比基线减少20%。两种方法均采用相同的反向KL匹配目标函数。
Distribution Matching Distillation for Continuous Diffusion Language Models
Continuous diffusion language models generate all tokens in parallel, yet high-quality generation can still require hundreds of network evaluations (NFEs). We study how distributional distillation can reduce this cost by exploiting the student's probabilistic token outputs. Our unified formulation connects the student's output parameterization to the resulting gradient estimators and yields two methods with the same student architecture and reverse-KL matching objective: Simplex-DMD uses continuous token relaxations and pathwise gradients, while Reinforce-DMD uses categorical sampling and REINFORCE with a learned density ratio. We develop both methods for multi-step generation and investigate the training and sampling choices associated with each parameterization. On OpenWebText, for sequences of 1,024 tokens, Simplex-DMD achieves a generative perplexity of 45.6 at a unigram entropy of 5.44 nats in just 4 NFEs, a 49% reduction relative to the strongest evaluated diffusion baseline at matched entropy and sampling budget. Reinforce-DMD improves the frontier at larger budgets, reaching a generative perplexity of 14.9 at an entropy of 5.00 nats with 256 NFEs, a 20% reduction under the same comparison protocol.
