Update README.md

README.md

@@ -26,8 +26,29 @@ For the same prompt, a response with higher reward score has higher quality than
 
 A HuggingFace Transformers compatible version converted from this model is available at [https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward-HF](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward-HF)
 
-Try hosted inference for free at [build.nvidia.com](https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-reward) - it comes with an OpenAI-compatible API interface!
+Try hosted inference for free at [build.nvidia.com](https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-reward) - it comes with an OpenAI-compatible API interface and simply signing up gets you 100k free API calls to this model.
 
+Using this reward model for RLHF (specifically, REINFORCE), we were able to tune a Llama-3.1-70B-Instruct model to reach [AlpacaEval 2 LC](https://tatsu-lab.github.io/alpaca_eval/) of 57.6, [Arena Hard](https://github.com/lmarena/arena-hard-auto) of 85.0 and [GPT-4-Turbo MT-Bench](https://github.com/lm-sys/FastChat/pull/3158) of 8.98, which are known to be predictive of [LMSys Chatbot Arena Elo](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)
+
+As of 1 Oct 2024, this model is #1 on all three automatic alignment benchmarks, edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet.
+
+See details on our paper at [https://arxiv.org/abs/2410.01257](https://arxiv.org/abs/2410.01257) - as a preview, this model can correctly the question ```How many r in strawberry?``` without specialized prompting or additional reasoning tokens:
+
+```
+A sweet question!
+Let’s count the “R”s in “strawberry”:
+1. S
+2. T
+3. R
+4. A
+5. W
+6. B
+7. E
+8. R
+9. R
+10. Y
+There are **3 “R”s** in the word “strawberry”.
+```
 
 
 ## Terms of use
@@ -37,7 +58,7 @@ By accessing this model, you are agreeing to the LLama 3.1 terms and conditions
 
 ## RewardBench Primary Dataset LeaderBoard
 
-As of 30 Sept 2024, Llama-3.1-Nemotron-70B-Reward performs best Overall on RewardBench as well as with strong performance in Chat, Safety and Reasoning categories among the models below.
+As of 1 Oct 2024, Llama-3.1-Nemotron-70B-Reward performs best Overall on RewardBench as well as with strong performance in Chat, Safety and Reasoning categories among the models below.
 
  | Model  | Type of Data Used For Training |  Overall | Chat | Chat-Hard | Safety | Reasoning | 
 |:-----------------------------|:----------------|:-----|:----------|:-------|:----------|:-----------------------|
@@ -128,6 +149,16 @@ E-Mail: [Zhilin Wang](mailto:zhilinw@nvidia.com)
 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},
@@ -140,6 +171,7 @@ If you find this model useful, please cite the following works
 
 ## References(s):
 
+* [HelpSteer2-Preference](https://arxiv.org/abs/2410.01257)
 * [HelpSteer2](https://arxiv.org/abs/2406.08673)
 * [HelpSteer](https://arxiv.org/abs/2311.09528)
 * [SteerLM method](https://arxiv.org/abs/2310.05344)
@@ -199,4 +231,5 @@ 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/).
+