Reflect-RL: Two-Player Online RL Fine-Tuning for LMs
Runlong Zhou Simon S. Du Beibin Li
As language models (LMs) demonstrate their capabilities in various fields,
their application to tasks requiring multi-round interactions has become
increasingly popular. These tasks usually have complex dynamics, so supervised
fine-tuning (SFT) on a limited offline dataset does not yield good performance.
However, only a few works attempted to directly train the LMs within
interactive decision-making environments. We aim to create an effective
mechanism to fine-tune LMs with online reinforcement learning (RL) in these
environments. We propose Reflect-RL, a two-player system to fine-tune an LM
using online RL, where a frozen reflection model assists the policy model. To
generate data for the warm-up SFT stage, we use negative example generation to
enhance the error-correction ability of the reflection model. Furthermore, we
designed single-prompt action enumeration and applied curriculum learning to
allow the policy model to learn more efficiently. Empirically, we verify that
Reflect-RL outperforms SFT and online RL without reflection. Testing results
indicate GPT-2-xl after Reflect-RL also outperforms those of untuned
pre-trained LMs, such as Mistral 7B.