通过调控隐式令牌数量，实现扩散语言模型推理速度与质量的平衡。
Reasoning with Latent Tokens in Diffusion Language Models
- 引入隐式令牌概念，通过调节其数量实现推理速度与生成质量的平滑权衡。
- 在需要全局连贯性的推理任务中，该方法性能优于传统自回归模型。
- 该机制可迁移至自回归模型，显著提升其在复杂推理任务的表现。
离散扩散模型在语言建模上已可媲美自回归模型，甚至在需规划和全局连贯性的推理任务中表现更优，但推理时计算开销更大。我们发现这一权衡源于关键机制：扩散模型训练时会联合预测所有未知令牌的分布，包括当前步骤不会解码的令牌。若移除此联合预测，推理速度虽提升但性能下降，表明解码位置的准确预测依赖于对未解码令牌分布的联合推理。我们将这些视为隐式令牌，并提出调控其数量的方法，实验证明该方法能实现推理速度与样本质量间的平滑权衡。此外，我们通过辅助多令牌预测目标将隐式令牌引入自回归模型，在原本表现不佳的相同推理任务中取得显著提升。结果表明，尽管隐式令牌自然出现在扩散模型中，但其代表一种通用机制，可提升需全局连贯性或前瞻能力的任务性能。
原文摘要 · Abstract (English)
Discrete diffusion models have recently become competitive with autoregressive models for language modeling, even outperforming them on reasoning tasks requiring planning and global coherence, but they require more computation at inference time. We trace this trade-off to a key mechanism: diffusion models are trained to jointly predict a distribution over all unknown tokens, including those that will not actually be decoded in the current step. Ablating this joint prediction yields faster inference but degrades performance, revealing that accurate prediction at the decoded position relies on joint reasoning about the distribution of undecoded tokens. We interpret these as latent tokens and introduce a method for modulating their number, demonstrating empirically that this enables a smooth tradeoff between inference speed and sample quality. Furthermore, we demonstrate that latent tokens can be introduced into autoregressive models through an auxiliary multi-token prediction objective, yielding substantial improvements on the same reasoning tasks where they have traditionally struggled. Our results suggest that latent tokens, while arising naturally in diffusion, represent a general mechanism for improving performance on tasks requiring global coherence or lookahead.
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