This article by researchers from UC Berkeley and Google DeepMind explores leveraging reinforcement learning (RL) to enhance the capabilities of large language models (LLMs). The authors note that standard prompting and generation methods often don’t result in goal-directed agents and require extensive prompt tuning. This issue is especially noticeable in multi-turn conversations. The researchers propose RL as a potential solution to harness the modeling capabilities of LLMs and their internal representation of textual interactions. They introduce the LMRL-Gym benchmark, which provides a set of tasks to evaluate the performance of multi-turn RL for LLMs.
Publication date: 1 Dec 2023
Project Page: N/A
Paper: https://arxiv.org/pdf/2311.18232
