Update README.md

README.md

@@ -117,38 +117,7 @@ Pre-requisite: You would need at least a machine with 4 40GB or 2 80GB NVIDIA GP
     python scripts/deploy/nlp/query_inframework.py -mn nemotron -p "How many r in strawberry?" -mol 1024
     ```
     
-   
-
-
-
-## Contact
-
-E-Mail: [Zhilin Wang](mailto:zhilinw@nvidia.com)
-
-
-## Citation
-
-If you find this model useful, please cite the following works
-
-```bibtex
-@misc{wang2024helpsteer2preferencecomplementingratingspreferences,
-      title={HelpSteer2-Preference: Complementing Ratings with Preferences}, 
-      author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong},
-      year={2024},
-      eprint={2410.01257},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2410.01257}, 
-}
-@misc{wang2024helpsteer2,
-      title={HelpSteer2: Open-source dataset for training top-performing reward models}, 
-      author={Zhilin Wang and Yi Dong and Olivier Delalleau and Jiaqi Zeng and Gerald Shen and Daniel Egert and Jimmy J. Zhang and Makesh Narsimhan Sreedhar and Oleksii Kuchaiev},
-      year={2024},
-      eprint={2406.08673},
-      archivePrefix={arXiv},
-      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
-}
-```
+ 
 
 ## References(s):
 
@@ -190,6 +159,8 @@ v1.0
 
 # Training & Evaluation: 
 
+** REINFORCE implemented in NeMo Aligner
+
 ## Datasets:
 
 **Data Collection Method by dataset** <br>
@@ -214,4 +185,20 @@ v1.0
 ## Ethical Considerations:
 NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications.  When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse.  For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards.  Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
 
-Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
+
+## Citation
+
+If you find this model useful, please cite the following works
+
+```bibtex
+@misc{wang2024helpsteer2preferencecomplementingratingspreferences,
+      title={HelpSteer2-Preference: Complementing Ratings with Preferences}, 
+      author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong},
+      year={2024},
+      eprint={2410.01257},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2410.01257}, 
+}
+```