AI Engineering/Phase 14/Lesson 04

Tree of Thoughts and LATS: Deliberate Search

A single chain-of-thought trajectory has no room to backtrack. ToT (Yao et al., 2023) turns reasoning into a tree with self-evaluation on each node. LATS (Zhou et al., 2024) unifies ToT with ReAct and Reflexion under Monte Carlo Tree Search. Game of 24 goes from 4% (CoT) to 74% (ToT); LATS hits 92.7% pass@1 on HumanEval.

Build 1.3 hr Python (stdlib)

Prerequisites: Phase 14 · 01 (Agent Loop), Phase 14 · 03 (Reflexion)

#Learning Objectives

Frame reasoning as search: nodes are "thoughts," edges are "expansions," value is "how promising."
Implement a stdlib ToT-style BFS tree search with self-evaluation scoring.
Extend to a toy LATS MCTS loop with select / expand / simulate / backpropagate.
Decide when search is worth the token multiplier (Game of 24, code generation) and when a single trajectory is enough (simple Q&A).

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The text and code of this lesson are based on AI Engineering from Scratch by Rohit Ghumare, MIT licensed.