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