EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering
This work addresses computational inefficiency and extensibility issues in LLM steering for researchers and practitioners, representing an incremental improvement with production-ready infrastructure.
The paper tackles the problem of inefficient and limited LLM steering frameworks by introducing EasySteer, a unified system built on vLLM that achieves a 5.5-11.4x speedup and demonstrates effectiveness in applications like overthinking mitigation and hallucination reduction.
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 5.5-11.4$\times$ 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.