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--- |
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license: apache-2.0 |
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base_model: |
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- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B |
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pipeline_tag: question-answering |
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metrics: |
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- accuracy |
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--- |
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# OpenCausaLab/CauGym |
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<!-- Provide a quick summary of what the model is/does. --> |
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CauGym model is a model trained via GRPO (Group Relative Policy Optimization) on VERL framework (https://github.com/verl-project/verl), and it is specialized for causal inference. |
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## Model Details |
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- **Developed by:** OpenCausaLab |
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- **Model type:** LLM. |
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- **Language(s) (NLP):** Englsih. |
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### Model Sources |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/OpenCausaLab/CauGym |
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- **Paper :** https://www.arxiv.org/abs/2602.06337 |
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### Evaluation |
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We have evaluated this model on CALM benchmark and CauGym benchmark, and the evaluation metric is accuracy. |
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| Benchmark | ATE | CDE | ETT | NDE | NIE | PN | PS | |
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| :--- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | |
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| **CALM** | 0.990 | 0.994 | 0.900 | 0.940 | 0.930 | 0.928 | 0.866 | |
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| **CauGym-rephrased**| 0.948 | 0.982 | 0.856 | 0.890 | 0.888 | 0.778 | 0.816 | |
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| **CauGym-ommitted** | 0.935 | 0.963 | 0.837 | 0.934 | 0.838 | 0.900 | 0.907 | |
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| **CauGym-deconfounding** | 0.976 | 0.986 | 0.854 | 0.572 | 0.872 | 0.952 | 0.848 | |
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| **CauGym-redundant** | 0.972 | 0.966 | 0.918 | 0.850 | 0.888 | 0.934 | 0.910 | |
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| **CauGym-insufficient** | 0.884 | 0.902 | 0.686 | 0.696 | 0.958 | 0.940 | 0.954 | |
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## Citation |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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```latex |
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@misc{chen2026posttrainingtransformllmscausal, |
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title={Can Post-Training Transform LLMs into Causal Reasoners?}, |
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author={Junqi Chen and Sirui Chen and Chaochao Lu}, |
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year={2026}, |
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eprint={2602.06337}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2602.06337}, |
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} |
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``` |
