SLiC-HF, a novel approach utilizing Sequence Likelihood Calibration with Human Feedback, is introduced to enhance language models, proving effective for the TL;DR summarization task. This method serves as a simpler, more computationally efficient alternative to Reinforcement Learning from Human Feedback (RLHF) and can leverage human feedback data from different models akin to off-policy offline RL data. The paper positions SLiC-HF as a competitive alternative to the PPO RLHF implementation, offering easier implementation, tuning, and computational efficiency. It emphasizes the approach's advantages, including the use of calibration and cross-entropy loss in improving models like T5, and highlights its performance through metrics such as ROUGE, perplexity, and win rate, demonstrating its efficacy in automatic evaluation systems.