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Official code for ACL2026 Oral paper Revisiting Model Interpolation for Efficient Reasoning

[📜 Paper][🐱 GitHub]

Environment

Please follow the official guidance of Opencompass to set up a Python environment.

We use the lmdeploy backend. Please remember to set

pip install "opencompass[lmdeploy]"

Weights

Download the official weights from HuggingFace:

We recommend downloading via the huggingface-cli, such as

hf download Qwen/Qwen3-30B-A3B-Thinking-2507 --token $your_hf_token --local-dir weights/Qwen3-30B-A3B/Qwen3-30B-A3B-Thinking-2507

hf download Qwen/Qwen3-30B-A3B-Instruct-2507 --token $your_hf_token --local-dir weights/Qwen3-30B-A3B/Qwen3-30B-A3B-Instruct-2507

Then, run the mi.py:

python mi.py --model_b /path/to/your/projects/base_model --model_i /path/to/your/projects/finetuned_model --lambda_val 0.5 --output_dir /path/to/your/projects/merged_output

where lambda_val is the interpolation factor.

Evaluation

We employ the OpenCompass for evaluation.

You need to modify the config files first.

For example, in evaluation/qwen3_AIME.py, replace the paths with your folder, modify the gpus to fit your machine.

Then all you need is to run opencompass evaluation/qwen3_AIME.py and wait the final results.

Warnning: In this repo, we benchmark the Instruct-2507/Thinking-2507 version, which do not require setting 'enable_thinking'. If you try to evaluate the Qwen3 hybrid thinking model, such as Qwen3-4B, please fix the bugs in Opencompass and pass an extra enable_thinking following this repo.

License

We use the Apache‑2.0 license. Please also comply with the licenses of any upstream models and datasets.

☕️ Citation

If you find this repository helpful, please consider citing our paper:

@inproceedings{wu2026revisiting,
  title={Revisiting model interpolation for efficient reasoning},
  author={Wu, Taiqiang and Yang, Runming and Liu, Tao and Wang, Jiahao and Wong, Ngai},
  booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={8624--8638},
  year={2026}
}

For any questions, please pull an issue or email at takiwu@connect.hku.hk

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Official code for paper "Revisiting Model Interpolation for Efficient Reasoning"

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