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README.md

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 This repository contains the released models for the paper [GRAM: A Generative Foundation Reward Model for Reward Generalization 📝](). 
 
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 This training process is introduced above. Traditionally, these models are trained using labeled data, which can limit their potential. In this study, we propose a new method that combines both labeled and unlabeled data for training reward models. We introduce a generative reward model that first learns from a large amount of unlabeled data and is then fine-tuned with supervised data. Additionally, we demonstrate that using label smoothing during training improves performance by optimizing a regularized ranking loss. This approach bridges generative and discriminative models, offering a new perspective on training reward models. Our model can be easily applied to various tasks without the need for extensive fine-tuning. This means that when aligning LLMs, there is no longer a need to train a reward model from scratch with large amounts of task-specific labeled data. Instead, **you can directly apply our reward model or adapt it to align your LLM based on our [code](https://github.com/wangclnlp/GRAM/tree/main)**.