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[2602.18582] Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications
Summary
The paper presents Hierarchical Reward Design from Language (HRDL), a framework to align AI behavior with human specifications through enhanced reward design in reinforcement learning.
Why It Matters
As AI systems become more complex, ensuring they align with human values and expectations is crucial for responsible AI deployment. This research addresses the limitations of current reward design methods, offering a more nuanced approach to capturing human preferences in AI behavior.
Key Takeaways
- HRDL provides a framework for better aligning AI behavior with human specifications.
- L2HR is proposed as a solution to encode richer behavioral specifications.
- Experiments show improved task completion and adherence to human expectations.
- The research advances the field of human-aligned AI agents.
- Understanding reward design is essential for responsible AI development.
Computer Science > Artificial Intelligence arXiv:2602.18582 (cs) [Submitted on 20 Feb 2026] Title:Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications Authors:Zhiqin Qian, Ryan Diaz, Sangwon Seo, Vaibhav Unhelkar View a PDF of the paper titled Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications, by Zhiqin Qian and 3 other authors View PDF HTML (experimental) Abstract:When training artificial intelligence (AI) to perform tasks, humans often care not only about whether a task is completed but also how it is performed. As AI agents tackle increasingly complex tasks, aligning their behavior with human-provided specifications becomes critical for responsible AI deployment. Reward design provides a direct channel for such alignment by translating human expectations into reward functions that guide reinforcement learning (RL). However, existing methods are often too limited to capture nuanced human preferences that arise in long-horizon tasks. Hence, we introduce Hierarchical Reward Design from Language (HRDL): a problem formulation that extends classical reward design to encode richer behavioral specifications for hierarchical RL agents. We further propose Language to Hierarchical Rewards (L2HR) as a solution to HRDL. Experiments show that AI agents trained with rewards designed via L2HR not only complete tasks effectively but also better adhere to human specifications. Tog...