Global Selection, Local Credit: Decoupled Topology Search for Adaptive Test-Time Scaling

Feedback-guided inference-time tree search is widely used to improve the reasoning of large language models on complex tasks. Existing adaptive methods use feedback to allocate a limited inference budget between generating new solutions and revising existing ones. However, they still traverse the tree level by level to choose a solution to revise, even though a complete solution can serve directly as the starting point. Consequently, whether a candidate receives further computation depends on both its estimated value and its position in the search tree, which can lead to inefficient allocation and limit search performance. To allocate inference compute more efficiently, we propose Decoupled Topology Search (DTS), an inference-time framework that searches over complete solutions. Before each model call, DTS uses the available feedback to decide globally whether to generate a new solution or revise an existing one. If it chooses revision, it selects directly from all valid candidate solutions based on their current quality estimates and uncertainty, without traversing the tree level by level. DTS retains the links through which candidates were generated to propagate feedback and continually update their value estimates. We evaluate DTS on code generation, mathematical reasoning, and molecular optimization. At the same candidate-call budget, DTS outperforms repeated sampling and existing tree-search baselines. On LiveCodeBench v6, DTS improves the Pass@1 of Qwen3-14B by up to 7.2 percentage points over repeated sampling.