According to Andrew Ng, DeepLearning.AI released a 5-module course on LLM post-training taught by Sharon Zhou, VP of AI at AMD, and it is available now; source: Andrew Ng on X. According to the DeepLearning.AI course page, the curriculum covers supervised fine-tuning, reward modeling, RLHF, PPO, GRPO, LoRA, and evaluation design for pre- and post-deployment; source: DeepLearning.AI course page. According to Andrew Ng, post-training is the key technique used by frontier labs to turn base LLMs into helpful, reliable assistants and to upgrade demo-level 80% reliability to consistent performance; source: Andrew Ng on X. According to the DeepLearning.AI course page, learners will gain skills to align models with RLHF, use LoRA for efficient fine-tuning without retraining entire models, prepare datasets and synthetic data, and operate LLM production pipelines with go/no-go decision points and feedback loops; source: DeepLearning.AI course page.

According to Andrej Karpathy, current reinforcement learning practices make LLMs mortally terrified of exceptions, and he argues exceptions are a normal part of a healthy development process, as stated on Twitter on Oct 9, 2025. Karpathy urged the community to sign his LLM welfare petition to improve rewards in cases of exceptions, as stated on Twitter on Oct 9, 2025. The post includes no references to cryptocurrencies, tokens, or market data, indicating no direct market update from the source, as stated on Twitter on Oct 9, 2025.