Rényi Entropy: A New Token Pruning Metric for Vision Transformers

• URL: http://arxiv.org/abs/2603.27900v1
• Date: Sun, 29 Mar 2026 23:01:45 GMT
• Title: Rényi Entropy: A New Token Pruning Metric for Vision Transformers
• Abstract summary: We argue that the [] token can be unreliable in early layers where semantic representations are still immature.<n>We propose a training-free token importance metric, namely Col-Ln, which is derived from Rényi entropy.<n>Our approach consistently outperforms state-of-the-art pruning methods across diverse benchmarks.
• Score: 7.739339395706548
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision Transformers (ViTs) achieve state-of-the-art performance but suffer from the $O(N^2)$ complexity of self-attention, making inference costly for high-resolution inputs. To address this bottleneck, token pruning has emerged as a critical technique to accelerate inference. Most existing methods rely on the [CLS] token to estimate patch importance. However, we argue that the [CLS] token can be unreliable in early layers where semantic representations are still immature. As a result, pruning in the early layer often leads to inaccurate importance estimation and unnecessary information loss. In this work, we propose a training-free token importance metric, namely Col-Ln, which is derived from Rényi entropy that enables the identification of informative tokens from the first layer of the network, thereby enabling more reliable pruning in token reduction. Extensive experiments on ViTs and Large Vision-Language Models (LVLMs) demonstrate that our approach consistently outperforms state-of-the-art pruning methods across diverse benchmarks.

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