Hierarchical Reward Design from Language: Enhancing Alignment of Agent Behavior with Human Specifications

How AI completes tasks matters as much as whether it completes them. The paper addresses a critical gap in AI training: current methods focus primarily on task completion but often ignore how agents accomplish goals. This distinction is important for responsible deployment because an AI might technically succeed while violating human values or safety constraints.

Language provides a practical bridge between human preferences and AI behavior. The L2HR method translates natural language specifications from humans into reward functions that guide AI learning. This approach makes it easier for non-technical stakeholders to communicate their expectations directly, rather than requiring them to manually design complex mathematical reward functions.

Hierarchical structure enables AI to follow nuanced instructions in complex tasks. By organizing rewards hierarchically, the method allows AI agents to understand and follow detailed behavioral specifications across long, multi-step tasks—moving beyond simple "complete the objective" instructions to capture subtleties like "complete the task safely" or "complete it while minimizing resource use."

Experimental validation shows improved alignment without sacrificing performance. The paper demonstrates that agents trained with HRDL not only complete tasks effectively but also better adhere to human specifications, suggesting this approach doesn't require trading off task success for behavioral alignment.