Reinforcement Learning is all You Need

Post-training plays a crucial role in refining language models, ensuring they exhibit good reasoning capabilities, alignment with ethical and social values, and adaptability to user-specific preferences. Unlike pre-training, which demands extensive computational resources and large-scale datasets, post-training is a more efficient process that leverages targeted fine-tuning techniques such as reinforcement learning from human feedback (RLHF) [1, 2], instruction tuning [3], and Direct Preference Optimization (DPO) [4]. These approaches enable models to generalize better across various tasks and mitigate biases. Furthermore, post-training requires significantly low computational overhead compared to pre-training [5]. Reasoning has long been regarded as a cornerstone in the development of large language models (LLMs).