Zihan Qiu (Qwen Team, Alibaba Group), On the Functional Role of Outliers in Large Language Models

Large Language Models (LLMs) exhibit intriguing emergent phenomena in their high-dimensional activation spaces, most notably "attention sinks" (a few tokens receiving disproportionately large attention logits) and "residual sinks" (a few fixed dimensions with persistently large magnitudes). While often treated as anomalies or quantization hurdles, we propose a unified functional view: these outliers are essential mechanisms for high-dimensional rescaling.

Specifically, we demonstrate that a small fraction of outliers, coupled with normalization layers (e.g., Softmax, RMSNorm), effectively rescale the remaining non-outlier components—a phenomenon we term outlier-driven rescaling. Through interventional studies, we provide evidence that outliers function jointly with normalization and act primarily as rescaling factors rather than direct semantic contributors. Removing normalization eliminates the corresponding outliers but degrades training stability, whereas clipping outliers while retaining normalization similarly harms performance, indicating that outlier-driven rescaling is crucial for stabilizing high-dimensional optimization. Furthermore, this perspective provides explanations for the success of certain architectural modifications. Building on these insights, we show that outliers can be effectively absorbed into learnable parameters or mitigated via explicit gated rescaling, leading to improved training performance and enhanced quantization robustness.