Update README.md

README.md

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 We introduce AceMath, a family of frontier models designed for mathematical reasoning. The models in AceMath family, including AceMath-1.5B/7B/72B-Instruct and AceMath-7B/72B-RM, are <b>Improved using Qwen</b>. 
 The AceMath-1.5B/7B/72B-Instruct models excel at solving English mathematical problems using Chain-of-Thought (CoT) reasoning, while the AceMath-7B/72B-RM models, as outcome reward models, specialize in evaluating and scoring mathematical solutions.
 
-The AceMath-7B/72B-RM models are developed from their AceMath-7B/72B-Instruct models and trained on AceMath-RM-Training-Data using Bradley-Terry loss. The architecture employs standard sequence classification with a linear layer on top of the language model, using the final token to output a scalar score.pull
+The AceMath-7B/72B-RM models are developed from our AceMath-7B/72B-Instruct models and trained on AceMath-RM-Training-Data using Bradley-Terry loss. The architecture employs standard sequence classification with a linear layer on top of the language model, using the final token to output a scalar score.pull
 
 For more information about AceMath, check our [website](https://research.nvidia.com/labs/adlr/acemath/) and [paper](https://arxiv.org/abs/2412.15084).
 
@@ -30,7 +30,7 @@ For more information about AceMath, check our [website](https://research.nvidia.
 ### Evaluation & Training Data
 - [AceMath-RewardBench](https://huggingface.co/datasets/nvidia/AceMath-RewardBench), [AceMath-Instruct Training Data](https://huggingface.co/datasets/nvidia/AceMath-Instruct-Training-Data), [AceMath-RM Training Data](https://huggingface.co/datasets/nvidia/AceMath-RM-Training-Data)
 
-### Base Models
+### General Instruction Models
 - [AceInstruct-1.5B](https://huggingface.co/nvidia/AceInstruct-1.5B), [AceInstruct-7B](https://huggingface.co/nvidia/AceInstruct-7B), [AceInstruct-72B](https://huggingface.co/nvidia/AceInstruct-72B)
 
 ## Benchmark Results (AceMath-Instruct + AceMath-72B-RM)