Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models

This is a Plain English Papers summary of a research paper called Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models. If you like these kinds of analysis, you should subscribe to the AImodels.fyi newsletter or follow me on Twitter.

Overview

• Reinforcement Learning with Human Feedback (RLHF) is a prominent method for aligning Language Models (LMs), but it is an unstable and data-hungry process.


• The paper introduces Advantage-Leftover Lunch RL (A-LoL), a new class of offline policy gradient …

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