This research paper proposes a new method for training large language models (LLMs) to be safer and more aligned with human values. The authors call their method Rule Based Rewards (RBR), which involves using a set of AI-graded rules to define desired and undesired behaviors for the model. This approach avoids the need for large amounts of human data and allows for fine-grained control over the model's responses. The paper demonstrates that RBRs are effective in improving safety while minimizing instances of the model being overly cautious. They also show that RBRs can be used to improve safety behaviors in models that have a tendency to over-refuse or sometimes prefer unsafe outputs. The paper provides a detailed explanation of RBRs, its advantages and limitations, and presents experimental results comparing RBRs to traditional reinforcement learning from human feedback (RLHF) methods.