According to DeepLearning.AI (@DeepLearningAI), most large language models require post-training to effectively follow instructions, reason clearly, and ensure safe outputs. Their latest short course, led by Assistant Professor Banghua Zhu (@BanghuaZ) from the University of Washington and co-founder of Nexusflow (@NexusflowX), focuses on practical post-training techniques for large language models. This course addresses the business need for AI models that can be reliably customized for enterprise applications, regulatory compliance, and user trust by using advanced post-training methods such as reinforcement learning from human feedback (RLHF) and instruction tuning. Verified by DeepLearning.AI’s official announcement, this trend highlights significant market opportunities for companies seeking to deploy safer and more capable AI solutions in industries like finance, healthcare, and customer service.

According to Andrej Karpathy (@karpathy), maintaining strong regularization is crucial to prevent model degradation when applying Reinforcement Learning from Human Feedback (RLHF) in AI systems (source: Twitter, June 25, 2025). Karpathy highlights that insufficient regularization during RLHF can lead to 'slop,' where AI models become less precise and reliable. This insight underscores the importance of robust regularization techniques in fine-tuning large language models for enterprise and commercial AI deployments. Businesses leveraging RLHF for AI model improvement should prioritize regularization strategies to ensure model integrity, performance consistency, and trustworthy outputs, directly impacting user satisfaction and operational reliability.