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About Scalable Agent Alignment Via Reward Modeling: A Research Direction
This document outlines a research direction for solving the agent alignment problem using reward modeling. It proposes (1) learning a reward function from user feedback that captures their intentions, and (2) training a policy with reinforcement learning to optimize the learned reward function. Key challenges of scaling this approach to complex domains are discussed, along with approaches to address them. The goal is to develop agents that are scalable, economical, and provide a practical solution to alignment in the near term while also working towards a long term theoretical solution.
Detailed Information
| Author: | ['Prapamobile'] |
|---|---|
| Publication Year: | 2019 |
| Pages: | 30 |
| Language: | English |
| Format: | |
| Price: | FREE |
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