This tutorial explains how to design an advanced Tree-of-Thoughts (ToT) multi-branch reasoning agent using beam search, heuristic scoring, and depth-limited pruning techniques. It focuses on solving mathematical puzzles like the 24 game by transforming language model reasoning into a structured search process. The guide covers implementing state representation, branch generation with LLM tools or rule-based methods, defining heuristic scores for tasks, running ToT loops to expand, score, prune branches, and select top beams until reaching a solution or exhausting search limits. It concludes by providing a reusable ToT framework adaptable to various reasoning problems beyond the 24 game.
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