CoBa: Cost-Effective Test-Time Scaling via Compute-Balanced Routing

Test-time scaling strategies for LLM reasoning—generating more samples, longer chains of thought, or stronger verification—compete for a fixed compute budget, raising the question of where to best allocate resources. CoBa formulates this as a routing problem, first applying cheap verification broadly before directing only uncertain or high-value candidates to stronger, costlier verification. This benefits applications requiring efficient, high-accuracy reasoning under budget constraints, such as automated math and reasoning solvers, where CoBa matched or approached best-of-N sampling performance while using roughly half the compute, offering a practical framework for cost-effective test-time reasoning system design. Paper: https://arxiv.org/abs/2608.07424