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One obstacle to applying reinforcement learning algorithms to real-worldproblems is the lack of suitable reward functions. Designing such rewardfunctions is difficult in part because the user only has an implicitunderstanding of the task objective. This gives rise to the agent alignmentproblem: how do we create agents that behave in accordance with the user'sintentions? We outline a high-level research direction to solve the agentalignment problem centered around reward modeling: learning a reward functionfrom interaction with the user and optimizing the learned reward function withreinforcement learning. We discuss the key challenges we expect to face whenscaling reward modeling to complex and general domains, concrete approaches tomitigate these challenges, and ways to establish trust in the resulting agents.

Scalable agent alignment via reward modeling: a research direction