Last updated: April 2026 Reinforcement Learning from Human Feedback (RLHF) is a 3-stage training pipeline that aligns large language models with human preferences: first supervised fine-tuning, then reward model training on ranked outputs, and finally policy optimization with PPO. As of 2026, RLHF remains the conceptual foundation of LLM alignment, but production systems increasingly replace … Read more
Last updated: March 2026 AI in trading refers to using machine learning, natural language processing, and reinforcement learning to analyze financial market data, generate trading signals, and optimize execution. Unlike traditional algorithmic trading that follows static rules, AI trading systems learn from data and adapt to changing market conditions — but they are not crystal … Read more
Machine learning (ML) is a branch of artificial intelligence in which algorithms learn patterns from data — instead of following hard-coded rules — and improve their performance on a defined task as they process more examples. The three core paradigms are supervised learning (labeled data), unsupervised learning (unlabeled data) and reinforcement learning (reward-based interaction). In … Read more