Claim-Level Reliability Assessment for Efficient Test-Time Reasoning· 测试时推理的声明级可靠性评估
We propose claim-level falsification as a principle for test-time scaling and instantiate it through Claim-Level Reliability Assessment (CLR), a training-free framework that reallocates test-time compute from additional solution sampling to targeted verification. Since whole-trace evaluation often obscures decisive errors due to signal dilution from routine tokens, CLR condenses each reasoning trace into a compact set of decision-critical claims, thereby isolating its logical anchors. Furthermore, recognizing the inherent difficulty of generating entirely correct solutions under fixed model capabilities, CLR shifts the focus to semantic falsification. This approach exploits a fundamental asymmetry between solution construction and claim refutation. Constructing a valid solution requires a
提出声明级可靠性评估框架,用于测试时优化计算资源分配。
- 核心方法
- 通过将推理轨迹浓缩为决策关键声明集,CLR框架重新分配测试时计算资源,从额外的解决方案采样转向目标验证,关注语义驳斥而非完全正确的解决方案生成。
- 适合谁读
- 研究者 / 工程师
- 要解决的问题
- 解决在测试时如何高效地利用计算资源进行推理的问题,避免因常规令牌的信号稀释而掩盖关键错误。
- 关键实验
- 未提供
- 主要贡献
- 引入了声明级可靠性评估(CLR)的训练免费框架,有效提高了测试时推理的效率和准确性。
- 意义与局限
- 该方法有助于优化现有AI模型的测试流程,提高推理效率。然而,其局限在于可能未充分考虑到模型在不同场景下的适用性和泛化能力。