Revisiting Tree Search for LLMs: Gumbel and Sequential Halving for Budget-Scalable Reasoning

ArXi:2603.21162v1 Announce Type: new Neural tree search is a powerful decision-making algorithm widely used in complex domains such as game playing and model-based reinforcement learning. Recent work has applied AlphaZero-style tree search to enhance the reasoning capabilities of Large Language Models (LLMs) during inference, but we find that this approach suffers from a scaling failure: on GSM8K and Game24, accuracy drops as the search budget increases.