  We tackle the problem of aligning pre-trained large language models (LMs)
with human preferences. If we view text generation as a sequential
decision-making problem, reinforcement learning (RL) appears to be a natural
conceptual framework. However, using RL for LM-based generation faces empirical
challenges, including training instability due to the combinatorial action
space, as well as a lack of open-source libraries and benchmarks customized for
LM alignment. Thus, a question rises in the research community: is RL a
practical paradigm for NLP?
  To help answer this, we first introduce an open-source modular library,
RL4LMs (Reinforcement Learning for Language Models), for optimizing language
generators with RL. The library consists of on-policy RL algorithms that can be
used to train any encoder or encoder-decoder LM in the HuggingFace library
(Wolf et al. 2020) with an arbitrary reward function. Next, we present the GRUE
(General Reinforced-language Understanding Evaluation) benchmark, a set of 6
language generation tasks which are supervised not by target strings, but by
reward functions which capture automated measures of human preference. GRUE is
the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally,
we introduce an easy-to-use, performant RL algorithm, NLPO (Natural Language
Policy Optimization) that learns to effectively reduce the combinatorial action
space in language generation. We show 1) that RL techniques are generally
better than supervised methods at aligning LMs to human preferences; and 2)
that NLPO exhibits greater stability and performance than previous policy
gradient methods (e.g., PPO (Schulman et al. 2017)), based on both automatic
and human evaluations.
