  Reinforcement Learning from Human Feedback (RLHF) has proven to be a strong
method to align Pretrained Large Language Models (LLMs) with human preferences.
But training models with RLHF is computationally expensive, and an overall
complex process. In this work, we study RLHF where the underlying models are
trained using the parameter efficient method of Low-Rank Adaptation (LoRA)
introduced by Hu et al. [2021]. We investigate the setup of "Parameter
Efficient Reinforcement Learning" (PERL), in which we perform reward model
training and reinforcement learning using LoRA. We compare PERL to conventional
fine-tuning (full-tuning) across various configurations for 7 benchmarks,
including 2 novel datasets, of reward modeling and reinforcement learning. We
find that PERL performs on par with the conventional RLHF setting, while
training faster, and with less memory. This enables the high performance of
RLHF, while reducing the computational burden that limits its adoption as an
alignment technique for Large Language Models. We also release 2 novel thumbs
up/down preference datasets: "Taskmaster Coffee", and "Taskmaster Ticketing" to
promote research around RLHF.
