EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918365178626048 |
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| author | Xu, Haolei Mei, Xinyu Yan, Yuchen Zhou, Rui Zhang, Wenqi Lu, Weiming Zhuang, Yueting Shen, Yongliang |
| author_facet | Xu, Haolei Mei, Xinyu Yan, Yuchen Zhou, Rui Zhang, Wenqi Lu, Weiming Zhuang, Yueting Shen, Yongliang |
| contents | Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM's optimized inference engine, EasySteer achieves 10.8-22.3$\times$ speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_25175 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering Xu, Haolei Mei, Xinyu Yan, Yuchen Zhou, Rui Zhang, Wenqi Lu, Weiming Zhuang, Yueting Shen, Yongliang Computation and Language Artificial Intelligence Large language model (LLM) steering has emerged as a promising paradigm for controlling model behavior at inference time through targeted manipulation of hidden states, offering a lightweight alternative to expensive retraining. However, existing steering frameworks suffer from critical limitations: computational inefficiency, limited extensibility, and restricted functionality that hinder both research progress and practical deployment. We present EasySteer, a unified framework for high-performance, extensible LLM steering built on vLLM. Our system features modular architecture with pluggable interfaces for both analysis-based and learning-based methods, fine-grained parameter control, pre-computed steering vectors for eight application domains, and an interactive demonstration system. Through deep integration with vLLM's optimized inference engine, EasySteer achieves 10.8-22.3$\times$ speedup over existing frameworks. Extensive experiments demonstrate its effectiveness in overthinking mitigation, hallucination reduction, and other key applications. EasySteer transforms steering from research technique to production-ready capability, establishing critical infrastructure for deployable, controllable language models. |
| title | EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2509.25175 |