Sparse Internal Control of Language Models

Activation steering via sparse autoencoders (SAEs) enables behavioral control of large language models without task-specific fine-tuning, but standard methods apply the steering signal at every generated token, incurring constant per-token perturbation that risks degrading fluency. We ask: is dense intervention necessary? We introduce Stochastic Token Steering (STS), which gates each token independently with probability $p$, and Stochastic Block Steering (SBS), which gates a leading window once per sequence; neither requires a reward model or learned gating policy. Across two model families and two behavioral tasks, steering only a fraction of tokens recovers most of the behavioral effect of dense steering while incurring less fluency degradation. Across our experimental settings, we further find that the best-performing steering magnitude consistently rises as the intervention ratio falls.

Language models can perform a wide range of tasks at various levels of abstraction. They flexibly infer tasks from context, execute multiple tasks simultaneously, and select among competing goals. Understanding and controlling how models represent these tasks is central to aligning them with human objectives. Researchers can change model behaviour in many ways, but model steering, which manipulates intermediate task representations, links representation to behaviour most directly. Current steering methods typically intervene densely and assume linear effects, e.g., shifting every activation along a global direction in the residual stream. Because such interventions change thousands of neurons at once, they leave researchers to separate relevant from irrelevant neurons. We introduce Distributed Sparse Interventions (DSI), which steers models through a sparse set of individual neurons distributed across layers and attention heads. Finding such a set requires accounting for the selected neurons' interactions, even across layers; DSI achieves that by iteratively refining which neurons to intervene on and how strongly. Across 12 tasks and three instruction-tuned models, DSI activates task behaviour by identifying and intervening on fewer than 0.1% of neurons, outperforming dense steering vectors and often matching 10-shot in-context learning. Because DSI identifies explicit sets of neurons, it connects neuron-level computation to changes in latent representations and, ultimately, to desired outputs, allowing us to study how distributed computational components interact to produce steered behaviour. Our results demonstrate that steering can be viewed not only as a linear shift in representation space, but also as a targeted intervention on model computation, realised by adjusting a small set of interacting neurons whose coordinated activity spans multiple layers and contributes to behaviour.

Controllable generation requires language models to realize output characteristics such as reading level, politeness, and toxicity. Existing steering methods are often indirect, require access to internal activations, or depend on auxiliary trained models. We propose SWAI, a training-free inference-time method that addresses these limitations by steering directly in logit space using corpus-derived token statistics. SWAI computes z-normalized one-vs-rest log-odds scores from labeled corpora and biases high-scoring tokens only within the model's top-K candidate set, allowing control to favor target-characteristic tokens while preserving contextually plausible choices. Across readability, politeness, and toxicity control, SWAI consistently improves over prompt-based and prior logit-level baselines without modifying model parameters, accessing internal layers, or training an auxiliary model. Selectivity and lookup-table ablations show that the gains come from target-specific statistical scores rather than generic logit perturbation. These results indicate that effective steering does not require learned controllers when the logit intervention is guided by target-specific statistics under high-probability candidates.