The paper outlines a research direction for the agent alignment problem based on reward modeling, in which a reward function is learned from interaction with the user and then optimized with reinforcement learning.
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Scalable agent alignment via reward modeling: a research direction → states its contribution → The paper outlines a research direction for the agent alignment problem based on reward modeling, in which a reward function is learned from interaction with the user and then optimized with reinforcement learning.
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