Advancing the Pareto Frontier of Test-Time Scaling with Multi-Path Plan Aggregation

Abstract

Test-time scaling seeks to improve reasoning through additional inference-time computation, commonly using solution sampling with majority voting to select the final answer. However, repeated sampling can reproduce correlated errors, limiting the accuracy gains from additional computation. We introduce Multi-Path Plan Aggregation (MPPA), which learns planning and answer selection using separate LoRA adapters on a shared frozen backbone. The planner jointly generates $K$ short plans, each guiding a separate solution generated by the frozen executor; the aggregator then scores the resulting solutions and selects the final answer. Joint planning encourages complementary approaches suited to the executor's capabilities, while learned selection uses reasoning evidence to identify correct candidates. Alternating optimization improves planning and aggregation through supervised finetuning and reinforcement learning. Across five mathematics and science benchmarks and three backbones, MPPA improves accuracy--token trade-offs compared with training-free and training-based baselines. With three plans, it achieves higher average accuracy than eight directly sampled solutions with majority voting or the same learned aggregator, while reducing output tokens by 47--54%. Evaluation with task-provided subtasks further demonstrates higher final-task accuracy than direct sampling with majority voting or learned aggregation at either the task or subtask level.