arXiv cs.AI· Jiahao Lu, Mohan Kankanhalli·· 9 天前AI 评分29
Agentic-TTT：为测试时训练学习测试时策略
Agentic-TTT: Training test-time policy for test-time training
AI 导读
Agentic-TTT 将 TTT 流程转化为可调用工具，把累积技能视为不断演化的部署环境，并依据决策带来的效用增益训练测试时策略，决定何时启用 TTT、调用哪种算法以及能否复用已有技能。在其自建 benchmark 上，该策略相比骨干模型效用接近翻倍，还能在效用与算力之间做权衡，并泛化到训练中未见过的领域。
正文 · 原文
Abstract:Test-time training (TTT) adapts an LLM's parameters using signals derived from test inputs, and can make striking improvements in pre-specified settings such as IMO competitions or designated open problems. By turning deployment experience into parameter updates, TTT provides a direct mechanism for model-level self-improvement. Yet TTT is not universally beneficial: each TTT algorithm works in different settings, and applying an ill-suited method could waste test-time compute or even damage model performance. Therefore, such parameter-level self-improvement requires agency: the model must decide when TTT is warranted, which algorithm to invoke, and whether an existing skill can be reused. To fill this gap, we introduce Agentic-TTT, which learns a test-time policy to govern those decisions. Agentic-TTT turns TTT procedures into callable tools, treats accumulated skills as an evolving deployment environment, and trains its policy using the observed utility gains from its decisions. On our benchmark, Agentic-TTT nearly doubles the utility over the backbone model, learns to trade off utility against compute, and generalizes to domains unseen during training. Together, these results point toward autonomous self-improvement: models that can decide how to learn from their own deployment experience.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computation and Language (cs.CL) |
| Cite as: | arXiv:2610.12002 [cs.LG] |
| (or arXiv:2610.12002v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.12002 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jiahao Lu [view email]
[v1]
Thu, 8 Oct 2026 14:06:26 UTC (821 KB)
来源：arXiv cs.AI · arxiv.org