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Safe RLHF: Safe Reinforcement Learning from Human Feedback
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A peer-reviewed paper (OpenReview) proposing a constrained RLHF framework relevant to practitioners training safer language models; useful for those studying the helpfulness-harmlessness trade-off in LLM alignment.
Metadata
Importance: 68/100conference paperprimary source
Summary
Safe RLHF proposes a framework that explicitly decouples helpfulness and harmlessness in RLHF training by separately modeling reward and cost functions, then optimizing them via constrained reinforcement learning. This approach aims to balance the competing objectives of being helpful while avoiding harmful outputs, addressing a key tension in aligning language models. The method demonstrates improved safety-helpfulness trade-offs compared to standard RLHF.
Key Points
- •Separates reward (helpfulness) and cost (harmlessness) signals from human feedback rather than conflating them into a single reward model
- •Uses constrained RL optimization to maximize helpfulness subject to safety constraints, avoiding over-restriction of model capabilities
- •Introduces a human annotation protocol to collect preference data along both helpfulness and harmlessness dimensions simultaneously
- •Empirically shows that Safe RLHF achieves better Pareto-optimal trade-offs between helpfulness and safety than standard RLHF baselines
- •Addresses the fundamental tension in RLHF where safety and helpfulness objectives can conflict, requiring explicit constraint handling
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| RLHF | Research Area | 63.0 |
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Safe RLHF: Safe Reinforcement Learning from Human Feedback
Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, Yaodong Yang
Published: 16 Jan 2024, Last Modified: 17 Apr 2024ICLR 2024 spotlightEveryoneRevisionsBibTeX
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Keywords: Safe Reinforcement Learning, Reinforcement Learning from Human Feedback, Large Language Model, AI Safety
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TL;DR: Safe Reinforcement Learning from Human Feedback
Abstract: 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 crowd workers' 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.
Code is available at https://github.com/PKU-Alignment/safe-rlhf.
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