  Learning from human feedback has been shown to be effective at aligning
language models with human preferences. Past work has often relied on
Reinforcement Learning from Human Feedback (RLHF), which optimizes the language
model using reward scores assigned from a reward model trained on human
preference data. In this work we show how the recently introduced Sequence
Likelihood Calibration (SLiC), can also be used to effectively learn from human
preferences (SLiC-HF). Furthermore, we demonstrate this can be done with human
feedback data collected for a different model, similar to off-policy, offline
RL data. Automatic and human evaluation experiments on the TL;DR summarization
task show that SLiC-HF significantly improves supervised fine-tuning baselines.
Furthermore, SLiC-HF presents a competitive alternative to the PPO RLHF
implementation used in past work while being much simpler to implement, easier
to tune and more computationally efficient in practice.
