How does tree-of-thought prompting extend chain-of-thought reasoning?

Tree-of-thought (ToT) treats reasoning as a search tree instead of a single linear chain. The model generates several candidate intermediate "thoughts" at each step, evaluates or scores them (often by prompting the model to judge promise or validity), and explores the most promising branches using search such as breadth-first or depth-first, with backtracking when a branch looks unproductive. This suits problems needing exploration, lookahead, or planning, like puzzles (Game of 24), where a single greedy chain often fails. The cost is many more model calls and a controller to manage expansion, evaluation, and pruning. ToT generalizes self-consistency (which only votes over full chains) by allowing deliberate branching and abandoning of partial paths, trading substantial compute for higher solve rates on hard tasks.