ReBalance Steering Vectors

Steering vectors for Efficient Reasoning with Balanced Thinking (ICLR 2026)

Overview

This repository provides steering vectors only for ReBalance.

It does not include:

• base model weights,
• inference/training code,
• datasets.

Use this repository together with the official codebase:

• Code: https://github.com/yu-lin-li/ReBalance
• Inference script: transformer_inference_steer_dp.py

Available Steering Vectors

Base model	Vector path	Recommended --steer_layer
DeepSeek-R1-Distill-Qwen-1.5B	vectors/DeepSeek-R1-Distill-Qwen-1.5B/steer_vector_layer19_conf_mixed.pt	19
DeepSeek-R1-Distill-Qwen-7B	vectors/DeepSeek-R1-Distill-Qwen-7B/steer_vector_layer22_conf_mixed.pt	22
QwQ-32B	vectors/QwQ-32B/steer_vector_layer58_conf_mixed.pt	58

Local Directory Layout (after download)

ReBalance/
├── transformer_inference_steer_dp.py
└── vectors/
    ├── DeepSeek-R1-Distill-Qwen-1.5B/
    │   └── steer_vector_layer19_conf_mixed.pt
    ├── DeepSeek-R1-Distill-Qwen-7B/
    │   └── steer_vector_layer22_conf_mixed.pt
    └── QwQ-32B/
        └── steer_vector_layer58_conf_mixed.pt

Download

Option 1: Clone the full model repository

git lfs install
git clone https://huggingface.co/Yulin-Li/ReBalance

Then copy the vectors/ folder into your local ReBalance/ root directory.

Option 2: Download only the vectors with huggingface_hub

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="Yulin-Li/ReBalance",
    repo_type="model",
    allow_patterns="vectors/*",
    local_dir="."
)

Quick Usage with ReBalance

python transformer_inference_steer_dp.py \
  --model_name_or_path 'DeepSeek-R1-Distill-Qwen-1.5B' \
  --dataset_dir "./Data/" \
  --output_path "./outputs" \
  --dataset "Math_AIME2024" \
  --max_generated_tokens 16000 \
  --num_gpus 8 \
  --steer_vector_path ./vectors/DeepSeek-R1-Distill-Qwen-1.5B/steer_vector_layer19_conf_mixed.pt \
  --steer_layer 19 \
  --steer_coef -1

Paper, Code, and Project Links

• Paper (Hugging Face): https://huggingface.co/papers/2603.12372
• Paper (Open Review): https://openreview.net/forum?id=cJseWJJ5IM
• Code: https://github.com/yu-lin-li/ReBalance
• Project page: https://rebalance-ai.github.io

Intended Use

• Research and reproducibility for ReBalance.
• Experiments on reasoning efficiency and accuracy trade-offs.
• Comparative studies on overthinking mitigation.

Citation

If you find ReBalance useful in your research, please cite our paper:

@article{li2026efficient,
  title={Efficient Reasoning with Balanced Thinking},
  author={Li, Yulin and Tu, Tengyao and Ding, Li and Wang, Junjie and Zhen, Huiling and Chen, Yixin and Li, Yong and Tian, Zhuotao},
  booktitle={Proceedings of the 14th International Conference on Learning Representations},
  year={2026}
}