算力有限时该先验证还是再生成？CoBa用路由策略帮你分配，省近一半token，准确率不掉。
CoBa是一种计算平衡路由策略，将测试时推理视为算力分配问题，决定下一步算力用于生成、验证还是停止。在涵盖MATH-500、AIME 2024/2025、AMC 2023及程序符号推理的3,129项评估中，CoBa-Routed-Strong达到85.13%的宏平均准确率，仅比自评估加权投票代理低0.07个百分点，却节省49.1%的参数加权token。与最佳16选多数投票相比，准确率差距在0.01个百分点以内，同时减少58.9%的参数加权token。配对bootstrap检验表明其显著优于单样本解码，但距池化oracle仍有提升空间。
CoBa: Cost-Effective Test-Time Scaling via Compute-Balanced Routing
Test-time scaling is often implemented by spending more compute along one axis: sampling more solutions, extending a chain of thought, or applying a stronger evaluator. Under a fixed inference budget, these choices compete. This paper formulates test-time reasoning as a compute-allocation problem in which a system must decide whether the next unit of compute should be spent on generation, verification, or stopping. We introduce CoBa, a compute-balanced routing policy that first obtains a small set of candidates, applies cheap verification broadly, and routes uncertain or high-value candidates to stronger verification. On 3,129 example-generator evaluations spanning MATH-500, AIME 2024/2025, AMC 2023, and procedural symbolic reasoning, CoBa-Routed-Strong reaches 85.13% macro accuracy, statistically matching a self-evaluation weighted-voting proxy at 85.20% while using 49.1% fewer parameter-weighted tokens. It also matches best-of-16 majority voting within 0.01 macro-accuracy points while using 58.9% fewer parameter-weighted tokens; paired tests retain a small best-of-16 edge at substantially higher cost. Paired bootstrap tests show significant gains over single-sample decoding, while the remaining gap to the pool oracle exposes headroom for sharper routing. For local reasoning systems, test-time scaling becomes a question of where the next computation is most valuable.