Replay Failures as Successes: Sample-Efficient Reinforcement Learning for Instruction Following

One-line Summary

The paper introduces Hindsight instruction Replay (HiR), a method that improves reinforcement learning for instruction-following tasks by converting failed attempts into successes, enhancing sample efficiency and reducing computational costs.

Plain-language Overview

This research focuses on improving how computers learn to follow complex instructions using a method called reinforcement learning. Typically, these systems need many examples of successful instruction-following to learn effectively, but they often struggle to produce such examples initially. The new method, Hindsight instruction Replay (HiR), cleverly reinterprets failed attempts as successes by focusing on the parts of the instructions that were correctly followed. This approach allows the system to learn more efficiently, using less computing power, and shows promising results in various tasks.

Technical Details