Scalable agent alignment via reward modeling: a research direction

TL;DR

This paper proposes a research direction for scalable agent alignment by learning reward functions from user interactions and optimizing them with reinforcement learning, addressing the challenge of aligning agents with user intentions in complex domains.

Contribution

It introduces a high-level research framework focused on reward modeling to improve agent alignment, highlighting key challenges and potential solutions for scaling in complex environments.

Findings

Identifies key challenges in scaling reward modeling

Proposes approaches to mitigate alignment difficulties

Discusses methods to establish trust in aligned agents

Abstract

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.