CoT Reasoning

Chain-of-thought reasoning studies how language models break complex problems into steps, use intermediate information, and produce answers that can be checked or acted on. Approaches include prompting models to show their work, training on reasoning traces with supervised learning or reinforcement learning, and using structured methods such as code, symbolic rules, trees, or latent reasoning states. Researchers test these methods on tasks including mathematics, multi-hop question answering, coding, clinical decisions, and visual or spatial understanding, while developing verification and adaptive-compute methods to control cost. A central tension is that longer or more detailed traces do not reliably improve accuracy or explain how a model reached its answer; they can also amplify errors, bias, or unsupported claims.