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RLHF-Reward-Modeling is an open-source research framework focused on training reward models used in reinforcement learning from human feedback for large language models. In RLHF pipelines, reward models are responsible for evaluating generated responses and assigning scores that guide the model toward outputs that better match human preferences. The repository provides training recipes and implementations for building reward and preference models using modern machine learning frameworks. It supports multiple optimization strategies commonly used in alignment pipelines, including reinforcement learning with PPO, iterative supervised fine-tuning using rejection sampling, and direct preference optimization methods. The project also includes evaluation results showing that the trained reward models can achieve competitive performance compared with other open-source alignment systems.
Features
- Training framework for reward and preference models in RLHF pipelines
- Support for PPO based reinforcement learning workflows
- Iterative supervised fine-tuning using rejection sampling
- Direct preference optimization training strategies
- Evaluation benchmarks for reward model performance
- GPU-accelerated training configurations for large language models
Project Samples
