Agentic-TTT: Training test-time policy for test-time training
- Published
- Source
- arXiv
- Paper number
- 1202
- Field
- AI / Agents
- arXiv ID
- 2610.12002
Key points
- Agentic-TTT turns TTT procedures into callable tools, treats accumulated skills as an evolving deployment environment, and trains a test-time policy by reinforcement learning.
- As the compute penalty rose from 0 to 0.4, the direct-answer rate increased monotonically from 11.8% to 39.8% while adaptation compute fell 35.5%.
- Moderate penalties (0.1 and 0.2) cut compute by about 30% while achieving higher mean utility than no penalty.
- Raising the tool weight from 1 to 4 improved final utility by 0.046 and reduced the invalid-action rate from 4.08% to 1.72%.
Paper links
External research summaries. These are not HDATF publications or measured product results.