According to @AndrewYNg, DeepLearning.AI has launched a new course titled 'Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training,' taught by @realSharonZhou, VP of AI at AMD (source: Andrew Ng, Twitter, Oct 28, 2025). The course addresses a critical industry need: post-training techniques that transform base LLMs from generic text predictors into reliable, instruction-following assistants. Through five modules, participants learn hands-on methods such as supervised fine-tuning, reward modeling, RLHF, PPO, GRPO, and efficient training with LoRA. Real-world use cases demonstrate how post-training elevates demo models to production-ready systems, improving reliability and user alignment. The curriculum also covers synthetic data generation, LLM pipeline management, and evaluation design. The availability of these advanced techniques, previously restricted to leading AI labs, now empowers startups and enterprises to create robust AI solutions, expanding practical and commercial opportunities in the generative AI space (source: Andrew Ng, Twitter, Oct 28, 2025).

According to DeepLearning.AI (@DeepLearningAI), a new course titled 'Fine-tuning and Reinforcement Learning for LLMs: Intro to Post-training' has been launched in partnership with AMD and taught by Sharon Zhou (@realSharonZhou). The course delivers practical, industry-focused training on transforming pretrained large language models (LLMs) into reliable AI systems used in developer copilots, support agents, and AI assistants. Learners will gain hands-on experience across five modules, covering the integration of post-training within the LLM lifecycle, advanced techniques such as fine-tuning, RLHF (reinforcement learning from human feedback), reward modeling, PPO, GRPO, and LoRA. The curriculum emphasizes practical evaluation design, reward hacking detection, dataset preparation, synthetic data generation, and robust production pipelines for deployment and system feedback loops. This course addresses the growing demand for skilled professionals in post-training and reinforcement learning, presenting significant business opportunities for AI solution providers and enterprises deploying LLM-powered applications (Source: DeepLearning.AI, Oct 28, 2025).

According to God of Prompt on Twitter, the statement 'This is what ChatGPT without makeup looks like' refers to viewing the base, unrefined version of ChatGPT before any specialized fine-tuning or reinforcement learning has been applied (source: @godofprompt, Oct 27, 2025). This highlights the significance of model training techniques such as RLHF (Reinforcement Learning from Human Feedback), which are crucial for making large language models like ChatGPT suitable for real-world business applications. Understanding the core capabilities and limitations of the raw AI model provides valuable insights for companies exploring custom AI solutions, model alignment, and optimization strategies to meet specific industry needs.

According to Andrej Karpathy (@karpathy), reinforcement learning (RL) processes applied to large language models (LLMs) have resulted in models that are overly cautious about exceptions, even in rare scenarios (source: Twitter, Oct 9, 2025). This reflects a broader trend where RLHF (Reinforcement Learning from Human Feedback) optimization penalizes any output associated with errors, leading to LLMs that avoid exceptions at the cost of developer flexibility. For AI industry professionals, this highlights a critical opportunity to refine reward structures in RLHF pipelines—balancing reliability with realistic exception handling. Companies developing LLM-powered developer tools and enterprise solutions can leverage this insight by designing systems that support healthy exception processing, improving usability, and fostering trust among software engineers.

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.