EasySteer: A Unified Framework for High-Performance and Extensible LLM
  Steering
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 5.5-11.4times 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.
68db4b13d2bf1f4b15ec741b
Haolei Xu
claimed_verified
2025-09-30T06:29:48.835Z
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xhl
user
zjuxhl
68db4b13d2bf1f4b15ec741c
Xinyu Mei
claimed_verified
2025-09-30T06:29:46.012Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/BGCasdq8kKQWaPHzkt6NS.webp
Xinyu Mei
user
xinyumei
68db4b13d2bf1f4b15ec741d
Yuchen Yan
claimed_verified
2025-09-30T06:29:51.653Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64098738342c26884c792c93/SxBUd-wLrl-PjQsrVYJte.jpeg
Yuchen Yan
user
yanyc
68db4b13d2bf1f4b15ec741e
Rui Zhou
68db4b13d2bf1f4b15ec741f
Wenqi Zhang
68db4b13d2bf1f4b15ec7420
Weiming Lu
68db4b13d2bf1f4b15ec7421
Yueting Zhuang
68db4b13d2bf1f4b15ec7422
Yongliang Shen
claimed_verified
2025-09-30T12:11:39.530Z
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/avatars/ce74839ba871f2b54313a670a233ba82.svg
Yongliang Shen
user
tricktreat
EasySteer: A Unified Framework for High-Performance and Extensible LLM
  Steering
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 5.5-11.4times 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.
EasySteer is a unified framework for efficient and extensible steering of large language models, offering significant speedups and improved functionality over existing methods.
large language model steering hidden states vLLM modular architecture pluggable interfaces analysis-based methods learning-based methods fine-grained parameter control steering vectors overthinking mitigation hallucination reduction deployable controllable language models