Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints

• URL: http://arxiv.org/abs/2510.08549v2
• Date: Fri, 10 Oct 2025 04:41:32 GMT
• Title: Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
• Authors: Zilin Kang, Chonghua Liao, Tingqiang Xu, Huazhe Xu,
• Abstract summary: We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models.<n>Our work validates output activation as a powerful tool for entropy control, opening a new direction for designing simpler and more robust algorithms.
• Score: 32.9206867882979
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models. Our approach demonstrates broad effectiveness across different domains: 1) for large language models(LLMs), boosting the AIME 2025 score for Qwen2.5-Math-7B by 37.4%; 2) for continuous control reinforcement learning agents, improving performance by more than 30% over strong baselines such as SAC on the challenging HumanoidBench; 3) for image classification, enhancing ImageNet top-1 accuracy by 0.69% for ResNet-50. These gains are achieved with a computational overhead of less than 7%. Our work validates output activation as a powerful tool for entropy control, opening a new direction for designing simpler and more robust algorithms.

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