Raisonnement en chaîne de pensée

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models studies prompts containing worked examples with intermediate steps. These examples show how to connect a question and final answer through an expressed solution, without changing the model’s weights during the prompt itself. The reported gains depend on the tasks, models, and scales tested, and do not establish that every small model or every problem benefits. Large Language Models Are Zero-Shot Reasoners studies eliciting steps with a short instruction without worked demonstrations. This is a prompting distinction rather than a new supervised training procedure. Self-Consistency Improves Chain of Thought Reasoning samples diverse reasoning paths and aggregates their final answers instead of selecting one greedy path. Agreement can help in tested settings, but several samples can also share the same error. Intermediate generated text can become context for later tokens in an autoregressive setup, allowing a solution to be expressed over several stages. That mechanism does not guarantee correct decomposition or explain every observed gain. The original DeepSeek-R1 report studies reinforcement learning and staged training for reasoning behavior, including R1-Zero’s RL experiment and R1’s additional stages. Training a reasoning model and prompting a fixed model with examples should therefore be distinguished. Finally, Language Models Don’t Always Say What They Think demonstrates explanations that omit biasing influences and rationalize affected answers in particular model tests. A fluent solution should be checked against the task, and faithfulness to the model’s underlying decision process is a separate evaluation question.