AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses
Score
76提出测试时能力迁移新范式，补充传统蒸馏短板，实证增益显著。
实验设计严谨，包含多基准验证及机制分析，但数据集类型单一。
无需参数更新即可提升弱模型性能，为低成本模型适配提供直接方案。
当前AI领域关注推理时计算和高效迁移，该方向契合前沿趋势。
arXiv预印本来自AI/CL/LG领域，虽未经同行评审但方法可复现。
Base 84.5 × T2 Source weight ×0.9 = 76
Curation line 70Selected
Why selected
The paper provides strong experimental evidence for the efficacy of test-time harnesses, with clear methodology and an important complement to existing distillation paradigms.
AI commentary
「What stands out here is turning 'scaffolding' from a systems-engineering term into a quantifiable transfer mechanism, with near-doubling gains that make you wonder how much training-time distillation cost could be saved. Engineers working on model deployment and inference optimization should watch this closely; for distillation researchers, it's a nudge that test-time design might be cheaper than parameter updates.」
Quick overview
This paper proposes a test-time capability transfer method where a stronger builder model constructs inference-time harnesses that help a weaker target model solve tasks more reliably, without any parameter updates. On four representative Theory-of-Mind benchmarks, average target-model performance nearly doubles from 0.49 to 0.91. Analysis shows that gains come primarily from offloading unstable reasoning into deterministic code, benchmark-specific routing, and strict answer-format enforcement, rather than from encouraging deeper reasoning or broader sampling. The authors further find that builder-model reasoning effort improves harness quality monotonically, platform effects are modest relative to builder capability, and weaker targets benefit most. These results suggest inference-time harness design is an important complement to conventional training-time distillation.
- Test-time capability transfer is achieved via strong model-built harnesses, without updating weak model parameters.
- Average performance on four Theory-of-Mind benchmarks nearly doubles from 0.49 to 0.91.
- Gains stem mainly from deterministic code offloading, task routing, and strict output format enforcement.
- Weaker target models benefit most, and builder reasoning effort monotonically improves harness quality.
Full text
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