arXiv:2502.18447v2 Announce Type: replace Abstract: Existing approaches to reward inference typically assume that humans provide demonstrations according to specific behavior models. However, humans often indicate their goals through a wide range of behaviors, from actions that are suboptimal due to poor planning or execution to behaviors intended to communicate goals rather than achieve them. One existing solution for inferring rewards from such behavior $\unicode{x2013}$ provided it is drawn from the same distribution at training and deployment $\unicode{x2013}$ is to construct a dataset of
The proliferation of advanced AI agents highlights the critical need for more robust reward inference mechanisms, especially given the complexities of human-AI interaction and safety concerns.
This research addresses a fundamental challenge in AI alignment and control, which is essential for developing intelligent systems that accurately understand and pursue human goals, even when human behavior is ambiguous or suboptimal.
This research proposes a new approach that infers rewards from human behavior without strict assumptions about optimal human decision-making, moving beyond traditional Inverse Reinforcement Learning paradigms.
- · AI safety researchers
- · Developers of advanced AI agents
- · Industries deploying AI for complex tasks
- · AI systems with rigid reward inference models
- · Current methods reliant on purely optimal human demonstrations
Improved understanding and alignment of AI systems with human intentions even from imperfect human input.
Accelerated development of more reliable and trustworthy AI agents capable of complex decision-making in real-world scenarios.
Reduced risk of unintended or harmful AI behaviors stemming from misinterpretations of human goals, fostering greater societal acceptance of advanced AI.
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