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 @DeepLearningAI, DeepLearning.AI announced a five-module course on fine-tuning and reinforcement learning for LLM post-training built in partnership with AMD and taught by Sharon Zhou, covering where post-training sits in the LLM lifecycle, RLHF, reward modeling, PPO, GRPO, LoRA, eval design, reward-hacking detection, red teaming, dataset preparation and synthetic data, and production pipelines for deployment with go or no-go decisions and feedback loops (source: @DeepLearningAI, Oct 28, 2025). According to @DeepLearningAI, the course aims to transform pretrained LLMs into reliable systems behind developer copilots, support agents, and AI assistants (source: @DeepLearningAI, Oct 28, 2025). According to @DeepLearningAI, AMD is named as the official partner for this course, providing a confirmed corporate tie-in relevant to AI infrastructure tracking by traders (source: @DeepLearningAI, Oct 28, 2025).

According to @rosstaylor90, reinforcement learning (RL) techniques like PPO have been applied successfully to Llama 2 base models, achieving over 90% accuracy on GSM8k with verifiable rewards. This highlights the effective use of RL in improving model performance, a critical insight for traders considering AI-backed trading strategies.