Update README.md

README.md

@@ -29,5 +29,13 @@ The dataset contains ~1M reasoning steps across 11 domains, where each step is a
 The data is structurally precise but generated via templates; it is most effective when combined with natural CoT datasets (e.g., PRM800k, Math-Shepherd).
 
 ## Citation
+
 If you use this dataset, please cite:
-Pisano et al., *PRM Meets Planning*
+
+```bibtex
+@inproceedings{prmsmeetplanning2026,
+  title={Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards},
+  author={Pisano, Raffaele and Navigli, Roberto},
+  booktitle={Proceedings of ACL 2026},
+  year={2026}
+}