Scalable agent alignment via reward modeling: a research direction
This addresses the challenge of applying reinforcement learning to real-world problems by providing a scalable approach to align agents with user intentions, though it is incremental as it builds on existing reward modeling concepts.
The paper tackles the agent alignment problem, where agents must behave according to user intentions despite implicit task objectives, by proposing a research direction centered on reward modeling to learn reward functions from user interaction and optimize them with reinforcement learning.
One obstacle to applying reinforcement learning algorithms to real-world problems is the lack of suitable reward functions. Designing such reward functions is difficult in part because the user only has an implicit understanding of the task objective. This gives rise to the agent alignment problem: how do we create agents that behave in accordance with the user's intentions? We outline a high-level research direction to solve the agent alignment problem centered around reward modeling: learning a reward function from interaction with the user and optimizing the learned reward function with reinforcement learning. We discuss the key challenges we expect to face when scaling reward modeling to complex and general domains, concrete approaches to mitigate these challenges, and ways to establish trust in the resulting agents.