EasySteer

A unified framework for high-performance and extensible LLM steering, built on vLLM.

EasySteer applies steering vectors — directions in a model's hidden-state space — during inference to shift model behavior without changing model weights. It extends vLLM's V1 engine so that steering runs at serving speed, with continuous batching, prefix caching, and CUDA-graph support.

Get started Paper (arXiv:2509.25175)

Why EasySteer

• High performance — 10.8–22.3× faster than existing steering frameworks through vLLM integration.
• One spec, every backend — the same SteeringSpec runs under eager, split, and in-graph CUDA-graph execution; declare your algorithms at launch and the engine picks the fastest tier that serves them, rejecting anything undeclared explicitly.
• Fine-grained control — token-level, position-specific, phase-aware (prompt vs. generation), and multi-vector steering.
• Modular algorithms — direct addition, linear maps, LoReFT, LM-Steer, projection-based erase/replace, MoE router steering; new algorithms plug in with two methods.
• Full research loop — capture hidden states, extract vectors (DiffMean, PCA, LAT, linear probe, SAE), train interventions (ReFT), and serve them, all in one repo.

The pieces

Component	What it is
vllm-steer/	Fork of vLLM with the steering engine (vllm.steer_vectors) and hidden-state capture
easysteer.hidden_states	Capture hidden states / MoE router logits from a running vLLM engine
easysteer.steer	Extract steering vectors from captured hidden states (analysis-based)
easysteer.reft	Train parameterized interventions on frozen models (learning-based)
frontend/	Web UI for interactive steering experiments
replications/	Notebook reproductions of published steering papers

A 30-second look

from vllm import LLM, SamplingParams
from vllm.steer_vectors import ApplySpec, SteeringSpec, VectorSpec

# Declare the steering algorithms this engine will serve; the engine
# derives the right CUDA-graph integration from the declaration.
llm = LLM(model="Qwen/Qwen2.5-1.5B-Instruct", enable_steer_vector=True,
          steer_algorithms=["direct"])

spec = SteeringSpec(vectors=[VectorSpec(
    source="vectors/happy_diffmean.gguf",
    scale=2.0,
    layers=list(range(10, 26)),
    apply=ApplySpec(phases=["prompt", "generation"]),
)])

out = llm.generate("Comfort Alice about her dog.",
                   steering=spec,
                   sampling_params=SamplingParams(max_tokens=128))

See the Quickstart for the full example, and the Steering guide for the complete spec language.

Citation

@article{xu2025easysteer,
  title={EasySteer: A Unified Framework for High-Performance and Extensible LLM Steering},
  author={Xu, Haolei and Mei, Xinyu and Yan, Yuchen and Zhou, Rui and Zhang, Wenqi and Lu, Weiming and Zhuang, Yueting and Shen, Yongliang},
  journal={arXiv preprint arXiv:2509.25175},
  year={2025}
}

Responsible use

Steering is dual-use. EasySteer is a research tool for model safety and controllability; behavioral modifications must be disclosed to end users and comply with applicable guidelines and law.