Official implementation of GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts.

GlimpRouter is a lightweight, training-free step-wise routing framework that uses the entropy of the initial token in each reasoning step to decide whether a small model or a large model should generate the step.

1. Clone this repository:

git clone git@github.com:Zengwh02/GlimpRouter.git
cd GlimpRouter

2. Set up your Python environment:

conda create -n glimp_router python=3.12 -y
conda activate glimp_router
pip install -r requirements.txt

3. (Optional) Download datasets using the helper script:

bash setup.sh

This project uses a simple config.json to pass experiment settings to src/main.py. You can generate a config with the provided script and then run the main entrypoint:

cd src
bash run.sh

The script writes config.json and launches main.py in the background, logging to ./logs/.

If you serve models locally with vLLM, fill in placeholders in server/serve.sh and run:

cd server
bash serve.sh

Notes:

• Replace YOUR_API_KEY, YOUR_MODEL_NAME_OR_PATH, YOUR_BASE_URL, and YOUR_PORT with your own values.
• Use the matching chat template from server/template/ or provide your own.

• src/main.py: entrypoint for GlimpRouter experiments; loads a dataset, performs routing, and writes results.
• src/glimprouter.py: core implementation of GlimpRouter step-wise routing and entropy-based decision logic.
• src/run.sh: example runner that generates config.json and starts main.py.
• server/serve.sh: helper script for spinning up a vLLM server.

The current code supports the following datasets:

• AIME24/AIME25 (math)
• MATH-500 (math)
• GPQA (general reasoning)
• LiveCodeBench v5/v6 (code generation)

To use datasets stored locally (e.g., GPQA or LiveCodeBench), edit the placeholders in src/main.py:

data_files="YOUR_DIRECTORY_OF_GPQA_DATASET"
data_files="YOUR_DIRECTORY_OF_LCB_DATASET"

For public datasets loaded via datasets.load_dataset, the dataset identifiers are set directly in the code. If you want to change them, update the dataset names in src/main.py.

config.json is read automatically by src/main.py. CLI arguments override config values. Example:

{
  "dataset_name": "lcbv5",
  "repeat_num": 6,
  "score_method": "first_token_entropy",
  "token_budget": 8192,
  "output_dir": "./results",
  "model_size": "32b",
  "small_model_size": "4b",
  "score_threshold": 1.0
}

.
├── data/                         # Local dataset files (optional)
├── server/
│   ├── serve.sh                  # vLLM server helper script
│   └── template/                 # Chat templates for different models
├── src/
│   ├── main.py                   # Experiment entrypoint
│   ├── glimprouter.py            # GlimpRouter core logic
│   └── run.sh                    # Example runner
├── requirements.txt
└── setup.sh

@misc{zeng2026glimprouterefficientcollaborativeinference,
      title={GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of Thoughts}, 
      author={Wenhao Zeng and Xuteng Zhang and Yuling Shi and Chao Hu and Yuting Chen and Beijun Shen and Xiaodong Gu},
      year={2026},
      eprint={2601.05110},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2601.05110}, 
}