Extracting Latent Timesteps in Diffusion Language Models

Diffusion Language Models (DLMs) encode denoising progress within their residual streams, even without explicit timestep conditioning. Probing these internal activations allows for steering the model along a low-dimensional subspace to modulate denoising progress.