metadata
license: mit
tags:
- rebalance
- steering-vector
- reasoning
- llm
- iclr-2026
language:
- en
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/
βββ transformer_inference_steer_dp.py
βββ vectors/
βββ DeepSeek-R1-Distill-Qwen-1.5B/
β βββ steer_vector_layer19_conf_mixed.pt
βββ DeepSeek-R1-Distill-Qwen-7B/
β βββ steer_vector_layer22_conf_mixed.pt
βββ QwQ-32B/
βββ 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}
}