发表机构
Spotify（Spotify）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对离散扩散模型的重写能力，提出变分Stackelberg离散扩散（VSDD）框架，通过领导者-跟随者博弈学习语义感知的破坏过程，在分子、文本和播放列表生成中显著提升有效性、降低困惑度并改善推荐指标。
AI 中文摘要
离散扩散模型具备重写（re-draft）能力，能够在生成过程中重新审视并修正早期生成的令牌。这一能力取决于定义去噪器学习修正目标的前向破坏过程。掩码扩散模型在令牌被揭开后即将其固定，而均匀扩散模型允许修订，但依赖于均匀随机的令牌替换。我们转而学习哪些替换对训练去噪器进行重写最为有用。我们提出了变分Stackelberg离散扩散（VSDD），一个用于学习语义感知破坏过程的框架。VSDD将训练形式化为一个领导者-跟随者博弈：领导者定义一个由去噪器的令牌嵌入参数化的马尔可夫破坏过程，而跟随者在破坏过程固定的情况下优化一个变分去噪目标。领导者根据去噪器从中学到后的改进程度来奖励破坏，而非根据当前去噪器重建它们的难易程度。我们在固定参考破坏过程下衡量这种改进，用一步梯度更新近似跟随者的响应，并使用得分函数估计器优化领导者。我们在分子、文本和播放列表生成任务上评估了VSDD。VSDD在分子有效性上显著优于均匀扩散和掩码扩散，在文本困惑度上相对于均匀扩散有所降低，同时与掩码扩散保持竞争力，并在离线播放列表推荐指标上取得了可观的改进。
英文摘要
Discrete diffusion models offer the ability to re-draft, revisiting and correcting earlier tokens throughout generation. This capability depends on the forward corruption process that defines what the denoiser learns to correct. Masked diffusion models fix tokens once they are unmasked, while uniform diffusion permits revisions but relies on uniformly random token substitutions. We instead learn which substitutions are most useful for training the denoiser to re-draft. We introduce Variational Stackelberg Discrete Diffusion (VSDD), a framework for learning a semantically aware corruption process. VSDD formulates training as a leader-follower game: the leader defines a Markovian corruption process parameterized by the denoiser's token embeddings, while the follower optimizes a variational denoising objective with the corruption process held fixed. The leader rewards corruptions based on how much the denoiser improves after learning from them, rather than on how easily the current denoiser can reconstruct them. We measure this improvement under a fixed reference corruption process, approximate the follower's response with a one-step gradient update, and optimize the leader using a score-function estimator. We evaluate VSDD across molecular, text, and playlist generation. VSDD substantially improves molecular validity over uniform and masked diffusion, reduces text perplexity relative to uniform diffusion while remaining competitive with masked diffusion, and achieves sizable improvements in offline playlist recommendation metrics.
