TDLM：让扩散语言模型复用历史表示，生成最快提速 7.15 倍
TDLM: Adaptive Temporal Integration for Diffusion Language Models
一篇提升扩散语言模型推理速度的论文：给 LLaDA 和 Dream 加个小模块，参数只多 0.84%，生成最快快 7.15 倍，做推理加速的可以看看。
扩散语言模型在多步去噪中反复计算未解析 token 的表示，但没有显式保留复用。TDLM（Temporal Self-Conditioning Diffusion Language Model）让每个未解析位置在冻结的去噪器内关注自身历史隐藏表示，使后续预测可利用已完成的计算。训练用自生成轨迹和跨熵目标，时间因果注意力支持训练时并行评估。在 LLaDA 上时间模块仅新增约 0.84% 可训练参数，在 LLaDA 和 Dream 上相比原模型生成最快提速 7.15 倍，答案质量保持竞争力。
TDLM: Adaptive Temporal Integration for Diffusion Language Models
Masked diffusion language models repeatedly compute representations for unresolved tokens, but conventional denoisers do not explicitly retain these representations for reuse across steps. We introduce Temporal Self-Conditioning Diffusion Language Model (TDLM), which lets each unresolved position attend to its own history of hidden representations within a frozen denoiser. This gives later predictions access to computation already performed. Training uses self-generated trajectories and a simple cross-entropy objective for tokens revealed between consecutively sampled decoding steps, while causal temporal attention permits parallel evaluation during training. On LLaDA, the temporal modules add only approximately 0.84% trainable parameters. Across LLaDA and Dream, the main comparison shows up to 7.15$\times$ faster generation than the corresponding original diffusion language model, with competitive answer quality on the evaluated tasks. These results motivate temporal memory as a practical way to make more effective use of repeated denoising computation.