Update README.md (#2)

- Update README.md (6d1c08facc89a3700f747e7feed14574ca9fe2eb)


Co-authored-by: Jiwoong Sohn <[email protected]>

README.md

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 # Med-PRM-Reward (Version 1.0)
-🚀 Med-PRM-Reward is among the first Process Reward Models (PRMs) specifically designed for the medical domain. Unlike conventional PRMs, it enhances its verification capabilities by integrating clinical knowledge through retrieval-augmented generation (RAG). Med-PRM-Reward demonstrates exceptional performance in scaling-test-time computation, particularly outperforming majority‐voting ensembles on complex medical reasoning tasks. Moreover, its scalability is not limited to Llama-3.1-8B-Instruct: it delivers similarly outstanding results in scaling-test-time computation across multiple other medical‐specialized models. Notably, when combined with llama-3-meerkat-8b-v1.0, it became the first sub-10B small language model to surpass a score of 80 on the MedQA (4-option) benchmark.
+🚀 Med-PRM-Reward is among the first Process Reward Models (PRMs) specifically designed for the medical domain. Unlike conventional PRMs, it enhances its verification capabilities by integrating clinical knowledge through retrieval-augmented generation (RAG). Med-PRM-Reward demonstrates exceptional performance in scaling-test-time computation, particularly outperforming majority‐voting ensembles on complex medical reasoning tasks. Moreover, its scalability is not limited to Llama-3.1-8B-Instruct: it delivers similarly outstanding results in scaling-test-time computation across multiple other medical‐specialized models. Notably, when combined with llama-3-meerkat-8b-v1.0, it became the first 8B model framework to surpass a score of 80 on the MedQA (4-option) benchmark.
 
 📄 Paper: [Med-PRM-Reward: Medical Reasoning Models with Stepwise, Guideline‑verified Process Rewards](https://huggingface.co/papers/2506.11474)