通过对比学习优化推理方法选择，提升大模型数学题解答准确率与效率
Reasoning Planning for Language Models
- 基于对比学习构建统一表示空间，融合模型推理能力与问题-方法匹配度
- 在多个数学推理任务上实现更高准确率，同时降低计算开销
- 适合需要高效高精度推理的AI系统研发人员参考
为特定查询选择合适的推理方法仍是语言模型生成中的关键挑战。现有方法通常生成多个候选答案并用聚合策略选出最终结果，普遍假设候选答案越多准确率越高。我们通过严格的理论分析重新审视该假设，在固定生成分布和候选数量条件下推导出标准聚合方法的准确率边界。基于此，提出EPIC框架——一种基于对比学习的集成规划方法，旨在学习一个同时捕捉模型推理能力与查询-方法兼容性的共享表示空间。EPIC将概率边界作为正则项，融入以效用为导向的优化目标，平衡准确率与计算成本。在多种数学推理任务上的实验表明，EPIC能持续选择最优推理方法，在提升准确率的同时减少计算开销。代码已开源：https://github.com/nguyenngocbaocmt02/EPIC。
原文摘要 · Abstract (English)
Selecting an appropriate reasoning method for a given query remains a key challenge in language model generation. Existing approaches typically generate multiple candidate responses and use an aggregation strategy to select the output answer, often assuming that more candidate answers yield higher accuracy. We revisit this assumption through a rigorous theoretical analysis, deriving accuracy bounds for standard aggregation methods under fixed generation distributions and candidate sizes. Building on these insights, we introduce EPIC, an Ensemble Planning with Contrastive learning framework to learn a shared representation space that captures both model reasoning abilities and query-method compatibility. EPIC incorporates our probability bounds as a regularizer in a utility-driven optimization that balances accuracy and computational cost. Experiments on diverse mathematical reasoning tasks show that EPIC consistently selects optimal reasoning methods, improving accuracy while reducing computational overhead. Our code can be found at https://github.com/nguyenngocbaocmt02/EPIC.
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