Activation Steering for Masked Diffusion Language Models

• URL: http://arxiv.org/abs/2512.24143v1
• Date: Tue, 30 Dec 2025 11:10:52 GMT
• Title: Activation Steering for Masked Diffusion Language Models
• Authors: Adi Shnaidman, Erin Feiglin, Osher Yaari, Efrat Mentel, Amit Levi, Raz Lapid,
• Abstract summary: Masked diffusion language models generate text through an iterative denoising process.<n>We present an activation-steering framework for MDLMs that computes layer-wise steering vectors from a single forward pass.<n>Experiments on LLaDA-8B-Instruct demonstrate reliable modulation of high-level attributes.
• Score: 1.0980666029958932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Masked diffusion language models (MDLMs) generate text through an iterative denoising process. They have recently gained attention due to mask-parallel decoding and competitive performance with autoregressive large language models. However, effective mechanisms for inference-time control and steering in MDLMs remain largely unexplored. We present an activation-steering framework for MDLMs that computes layer-wise steering vectors from a single forward pass using contrastive examples, without simulating the denoising trajectory. These directions are applied at every reverse-diffusion step, yielding an efficient inference-time control mechanism. Experiments on LLaDA-8B-Instruct demonstrate reliable modulation of high-level attributes, with ablations examining the effects of steering across transformer sub-modules and token scope (prompt vs.\ response).

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