A Comparative Analysis of Sparse Autoencoder and Activation Difference in Language Model Steering

• URL: http://arxiv.org/abs/2510.01246v1
• Date: Wed, 24 Sep 2025 08:31:31 GMT
• Title: A Comparative Analysis of Sparse Autoencoder and Activation Difference in Language Model Steering
• Authors: Jiaqing Xie,
• Abstract summary: We propose focusing on a single, most relevant SAE latent (top-1), eliminating redundant features.<n>We show that steering an SAE latent associated with reasoning reliably elicits step-by-step mathematical reasoning.<n>Our results demonstrate that SAEs outperform mean activation difference methods on mathematical reasoning benchmarks and match their performance on IF-Eval.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sparse autoencoders (SAEs) have recently emerged as a powerful tool for language model steering. Prior work has explored top-k SAE latents for steering, but we observe that many dimensions among the top-k latents capture non-semantic features such as punctuation rather than semantic attributes like instructions. To address this, we propose focusing on a single, most relevant SAE latent (top-1), eliminating redundant features. We further identify a limitation in constant SAE steering, which often produces degenerate outputs such as repetitive single words. To mitigate this, we introduce a token-wise decaying steering strategy, enabling more faithful comparisons with mean activation difference baselines. Empirically, we show that steering an SAE latent associated with reasoning reliably elicits step-by-step mathematical reasoning and enhances inference quality, functionally resembling the effect of appending a guiding token. Our results demonstrate that SAEs outperform mean activation difference methods on mathematical reasoning benchmarks and match their performance on IF-Eval.

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