  With the development of large language models (LLMs), striking a balance
between the performance and safety of AI systems has never been more critical.
However, the inherent tension between the objectives of helpfulness and
harmlessness presents a significant challenge during LLM training. To address
this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe
RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly
decouples human preferences regarding helpfulness and harmlessness, effectively
avoiding the crowdworkers' confusion about the tension and allowing us to train
separate reward and cost models. We formalize the safety concern of LLMs as an
optimization task of maximizing the reward function while satisfying specified
cost constraints. Leveraging the Lagrangian method to solve this constrained
problem, Safe RLHF dynamically adjusts the balance between the two objectives
during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we
demonstrate a superior ability to mitigate harmful responses while enhancing
model performance compared to existing value-aligned algorithms.
Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with
collected human preferences, significantly improving its helpfulness and
harmlessness according to human evaluations.
