Large language models (LLMs) have impressed us with their ability to generate human-like text, but they still struggle with complex reasoning tasks. Think of solving a math problem step-by-step or writing code that actually works—LLMs often fall short. A new research paper explores a fascinating approach inspired by AlphaGo, the AI that conquered the game of Go: tree search. Instead of generating text linearly, this method builds a tree of possible completions, exploring different branches based on the model's confidence in each step. Imagine the LLM brainstorming different paths to an answer and choosing the most promising one. This approach aims to improve output quality, reduce errors, and even boost creativity. Early experiments using the Phi-1.5 language model show that while the tree search method has potential, it also faces challenges. Just like AlphaGo needed immense computational power, tree search for LLMs requires significant resources. Moreover, simply having the LLM evaluate its own confidence isn’t enough. The research suggests incorporating a separate “evaluator model” trained on human-generated text to better judge the quality of different completions. This research is still in its early stages, but it offers a glimpse into a future where LLMs might finally achieve more human-like reasoning abilities. Challenges remain, particularly regarding computational costs and refining the evaluation process, but tree search could be a key step toward unlocking the true potential of AI. Future research will likely explore more sophisticated evaluation methods, including incorporating human feedback, and investigate how to efficiently scale this approach for larger, more complex tasks. The path to truly intelligent AI is long and winding, but tree search might just be a crucial turning point.