Update README.md

README.md

@@ -22,7 +22,7 @@ This model is ready for commercial use.
 
 OpenMath-Nemotron models achieve state-of-the-art results on popular mathematical benchmarks. We present metrics as pass@1 (maj@64) where pass@1
 is an average accuracy across 64 generations and maj@64 is the result of majority voting. 
-Please see our [paper](TODO) for more details on the evaluation setup.
+Please see our [paper](https://github.com/NVIDIA/NeMo-Skills/blob/main/recipes/openmathreasoning.pdf) for more details on the evaluation setup.
 
 | Model                         | AIME24 |  AIME25     |  HMMT-24-25     | HLE-Math    |
 |-------------------------------|-----------------|-------|-------|-------------|
@@ -106,16 +106,14 @@ Please note that these models have not been instruction tuned on general data an
 
 If you find our work useful, please consider citing us!
 
-TODO
-
-<!-- ```bibtex
-@article{toshniwal2024openmath2,
-  title   = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data},
-  author  = {Shubham Toshniwal and Wei Du and Ivan Moshkov and  Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman},
-  year    = {2024},
-  journal = {arXiv preprint arXiv:2410.01560}
+```bibtex
+@article{moshkov2025aimo2,
+  title   = {AIMO-2 Winning Solution: Building State-of-the-Art Mathematical Reasoning Models with OpenMathReasoning dataset},
+  author  = {Ivan Moshkov and Darragh Hanley and Ivan Sorokin and Shubham Toshniwal and Christof Henkel and Benedikt Schifferer and Wei Du and Igor Gitman},
+  year    = {2025},
+  journal = {arXiv preprint arXiv:TBD}
 }
-``` -->
+```
 
 ## Additional information