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arXiv · 2509.25727

Boundary-to-Region Supervision for Offline Safe Reinforcement Learning

Huikang Su·Dengyun Peng·Zifeng Zhuang·YuHan Liu·Qiguang Chen·Donglin Wang·Qinghe Liu
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Abstract

Offline safe reinforcement learning aims to learn policies that satisfy predefined safety constraints from static datasets. Existing sequence-model-based methods condition action generation on symmetric input tokens for return-to-go and cost-to-go, neglecting their intrinsic asymmetry: return-to-go (RTG) serves as a flexible performance target, while cost-to-go (CTG) should represent a rigid safety boundary. This symmetric conditioning leads to unreliable constraint satisfaction, especially when encountering out-of-distribution cost trajectories. To address this, we propose Boundary-to-Region (B2R), a framework that enables asymmetric conditioning through cost signal realignment . B2R redefines CTG as a boundary constraint under a fixed safety budget, unifying the cost distribution of all feasible trajectories while preserving reward structures. Combined with rotary positional embeddings , it enhances exploration within the safe region. Experimental results show that B2R satisfies safety constraints in 35 out of 38 safety-critical tasks while achieving superior reward performance over baseline methods. This work highlights the limitations of symmetric token conditioning and establishes a new theoretical and practical approach for applying sequence models to safe RL. Our code is available at https://github.com/HuikangSu/B2R.

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Huikang Su, Dengyun Peng, Zifeng Zhuang, YuHan Liu, Qiguang Chen, Donglin Wang, Qinghe Liu. 2025-09-30. Boundary-to-Region Supervision for Offline Safe Reinforcement Learning. https://arxiv.org/abs/2509.25727

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