Getting a model to show its work, and checking the work.
Generalises chain-of-thought into a search tree. The model proposes multiple partial solutions, evaluates them against each other, and can back out of a branch that is not working, rather than committing to one linear line of reasoning.
Instead of updating weights, the agent writes down what went wrong after a failed attempt and carries that reflection into the next attempt. The reflection is stored in natural language and used as additional context, so no gradient step is required.
Interleaves verbal reasoning traces with tool actions, so the model thinks about what to do next, does it, then reasons about what came back. The reasoning trace also becomes an auditable record of why the agent took each step.
Samples several independent reasoning paths for the same question and takes the most common answer, instead of trusting a single greedy chain. The intuition is that a correct derivation can be reached many ways while a wrong one is usually reached once.
Shows that asking a model to write out intermediate reasoning steps before answering substantially improves multi-step arithmetic, commonsense and symbolic reasoning, and that the behaviour appears without any fine-tuning at sufficient model scale.