A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models
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Computer Science > Machine Learning
Title:A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models
Abstract: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 50% of the tokens recovers most of the dense-steering effect while preserving fluency, and steering as few as 30% surpasses prompt-based control. The optimal steering magnitude scales inversely with the intervention ratio, revealing that SAE-mediated control is rate-limited: the behavioral outcome depends on cumulative signal dosage across a sequence.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.05615 [cs.LG] |
| (or arXiv:2607.05615v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2607.05615
arXiv-issued DOI via DataCite (pending registration)
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