Offline Safe Policy Optimization From Heterogeneous Feedback

• URL: http://arxiv.org/abs/2512.20173v1
• Date: Tue, 23 Dec 2025 09:07:53 GMT
• Title: Offline Safe Policy Optimization From Heterogeneous Feedback
• Authors: Ze Gong, Pradeep Varakantham, Akshat Kumar,
• Abstract summary: We introduce a framework that learns a policy based on pairwise preferences regarding the agent's behavior in terms of rewards, as well as binary labels indicating the safety of trajectory segments.<n>Our method successfully learns safe policies with high rewards, outperforming state-of-the-art baselines.
• Score: 35.454656807434006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Offline Preference-based Reinforcement Learning (PbRL) learns rewards and policies aligned with human preferences without the need for extensive reward engineering and direct interaction with human annotators. However, ensuring safety remains a critical challenge across many domains and tasks. Previous works on safe RL from human feedback (RLHF) first learn reward and cost models from offline data, then use constrained RL to optimize a safe policy. While such an approach works in the contextual bandits settings (LLMs), in long horizon continuous control tasks, errors in rewards and costs accumulate, leading to impairment in performance when used with constrained RL methods. To address these challenges, (a) instead of indirectly learning policies (from rewards and costs), we introduce a framework that learns a policy directly based on pairwise preferences regarding the agent's behavior in terms of rewards, as well as binary labels indicating the safety of trajectory segments; (b) we propose \textsc{PreSa} (Preference and Safety Alignment), a method that combines preference learning module with safety alignment in a constrained optimization problem. This optimization problem is solved within a Lagrangian paradigm that directly learns reward-maximizing safe policy \textit{without explicitly learning reward and cost models}, avoiding the need for constrained RL; (c) we evaluate our approach on continuous control tasks with both synthetic and real human feedback. Empirically, our method successfully learns safe policies with high rewards, outperforming state-of-the-art baselines, and offline safe RL approaches with ground-truth reward and cost.

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