Update README.md

README.md

@@ -10,6 +10,29 @@ base_model: internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B
 This model was converted to GGUF format from [`internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/internlm/OREAL-DeepSeek-R1-Distill-Qwen-7B) for more details on the model.
 
+---
+Introduction
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+We introduce OREAL-7B and OREAL-32B, a mathematical reasoning model series trained using Outcome REwArd-based reinforcement Learning, a novel RL framework designed for tasks where only binary outcome rewards are available. 
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+With OREAL, a 7B model achieves 94.0 pass@1 accuracy on MATH-500, matching the performance of previous 32B models. OREAL-32B further surpasses previous distillation-trained 32B models, reaching 95.0 pass@1 accuracy on MATH-500.  
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+Our method leverages best-of-N (BoN) sampling for behavior cloning 
+and reshapes negative sample rewards to ensure gradient consistency. 
+Also, to address the challenge of sparse rewards in long 
+chain-of-thought reasoning, we incorporate an on-policy token-level 
+reward model that identifies key tokens in reasoning trajectories for 
+importance sampling. For more details, please refer to our paper.
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+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)