arXiv:2605.21225v1 Announce Type: new Abstract: We address the problem of making a pre-trained reinforcement learning (RL) policy safety-aware by incorporating cost constraints without retraining it from scratch. While costs could be numerically encoded, we assume a more general setting is when costs are provided as preferences. Given a reward-optimized policy and a small dataset of preferred (low-cost) and dispreferred (high-cost) trajectories, our goal is to fine-tune the policy to generate low-cost behaviors while retaining high rewards. Unlike standard RLHF in language models, where prefer
The increasing sophistication and widespread deployment of AI necessitate robust safety mechanisms, leading to research focused on fine-tuning pre-trained models for alignment without full retraining.
This research addresses a critical challenge in AI safety, enabling more efficient and adaptable methods for embedding ethical and cost constraints into AI systems, particularly for autonomous agents.
The ability to fine-tune AI policies using preference-based costs, rather than numerically encoded ones, signifies a more intuitive and flexible approach to safety alignment in complex reinforcement learning environments.
- · AI developers
- · AI ethics researchers
- · Autonomous system manufacturers
- · Developers relying solely on brute-force retraining
- · Systems with poorly defined numerical cost functions
Improved safety and reliability of AI-powered systems through adaptable cost constraints.
Accelerated deployment of AI in sensitive applications where safety and ethical considerations are paramount.
Enhanced trust in AI systems could lead to wider societal acceptance and integration, potentially impacting regulatory frameworks and industry standards.
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