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README.md

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 # Model Card for Model ID
 
 <!-- Provide a quick summary of what the model is/does. -->
-**PPO-C** (PPO with Calibrated Reward Score) is an RLHF algorithm to mitigate verbalized overconfidence in RLHF-trained Large Language Models.
+**PPO-C** (PPO with Calibrated Reward Calculation) is an RLHF algorithm to mitigate verbalized overconfidence in RLHF-trained Large Language Models.
 PPO-C adjusts standard reward model scores during PPO training. It maintains a running average of past reward scores as a dynamic threshold to
 classify responses, and adjusts the reward scores based on model expressed verbalized confidence.
 Please refer to our preprint ([Taming Overconfidence in LLMs: Reward Calibration in RLHF](https://arxiv.org/abs/2410.09724)) and [repo](https://github.com/SeanLeng1/Reward-Calibration) for more details.