Meta-TTL: Meta-Learning Self-Improvement Policies for Language Agents
META-TTL introduces a framework called Meta-TTL to improve language agents through Test-Time Learning (TTL). Instead of hand-crafting adaptation rules, this system learns optimal policies by optimizing them against downstream performance during inference. It uses a bi-level optimization loop to discover self-improving strategies directly from task environments.