Meta-TTL: Meta-Learning Self-Improvement Policies for Language Agents
Test-Time Learning (TTL) enables language agents to iteratively refine their performance through repeated interactions with the environment at inference time. At the core of TTL is a self-improvement policy that updates the actor policy based on experience from previous episodes, thereby improving future behavior. Existing methods rely on hand-crafted self-improvement rather than optimizing them for downstream improvement. We argue that optimal self-improvement policies should be learned from task environments, not hand-engineered based on human intuition. To achieve this, we introduce \textbf{Meta-TTL}, a framework that formulates the discovery of effective self-improvement policies as a...
| Authors | Zhanzhi Lou, Hui Chen, Yibo Li et al. |
|---|---|
| Primary category | cs.LG |
| Published | 1 April 2026 |
| Last updated at source | 28 September 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.