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arXiv AI By Jiahao Lu, Mohan Kankanhalli

Agentic-TTT: Training test-time policy for test-time training

Read the original on arXiv AI →

Agentic‑TTT introduces a test‑time policy that decides when and how to apply test‑time training (TTT) to large language models. By treating TTT procedures as tools and learning from the utility gains of its decisions, the policy can trade off performance improvements against computational cost. On a benchmark, Agentic‑TTT nearly doubles the utility of the base model, adapts to unseen domains, and demonstrates autonomous self‑improvement capabilities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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