  Reinforcement learning from human feedback (RLHF) is a crucial technique in
aligning large language models (LLMs) with human preferences, ensuring these
LLMs behave in beneficial and comprehensible ways to users. However, a
longstanding challenge in human alignment techniques based on reinforcement
learning lies in their inherent complexity and difficulty in training. To
address this challenge, we present a simple yet effective Contrastive Learning
Framework for Human Alignment (CLHA) to align LLMs with human preferences
directly. CLHA employs a novel rescoring strategy to evaluate the noise within
the data by considering its inherent quality and dynamically adjusting the
training process. Simultaneously, CLHA utilizes pairwise contrastive loss and
adaptive supervised fine-tuning loss to adaptively modify the likelihood of
generating responses, ensuring enhanced alignment with human preferences. Using
advanced methods, CLHA surpasses other algorithms, showcasing superior
performance in terms of reward model scores, automatic evaluations, and human
assessments on the widely used ``Helpful and Harmless'' dataset.
