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arXiv · 2603.21461

DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment

James Wedgwood·Aashiq Muhamed·Mona T. Diab·Virginia Smith
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Abstract

Preference alignment is usually achieved by weight-updating training on preference data, which adds substantial alignment-stage compute and provides limited mechanistic visibility. We propose Dynamic SAE Steering for Preference Alignment (DSPA), an inference-time method that makes sparse autoencoder (SAE) steering prompt-conditional. From preference triples, DSPA computes a conditional-difference map linking prompt features to generation-control features; during decoding, it modifies only token-active latents, without base-model weight updates. Across Gemma-2-2B/9B and Qwen3-8B, DSPA improves MT-Bench and is competitive on AlpacaEval while preserving multiple-choice accuracy. Under restricted preference data, DSPA remains robust and can rival the two-stage RAHF-SCIT pipeline while requiring up to $4.47\times$ fewer alignment-stage FLOPs. Finally, we audit the SAE features DSPA modifies, finding that preference directions are dominated by discourse and stylistic signals, and provide theory clarifying the conditional-difference map estimate and when top-$k$ ablation is principled.

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James Wedgwood, Aashiq Muhamed, Mona T. Diab, Virginia Smith. 2026-09-06. DSPA: Dynamic SAE Steering for Data-Efficient Preference Alignment. https://arxiv.org/abs/2603.21461

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