BaT: Recursive Self-Improvement for Medical AI Agents
A recent preprint on arXiv presents Benchmark-as-Teacher (BaT), a system designed for recursive self-enhancement of long-horizon agents in medical imaging processes. The document, identified as arXiv:2608.16211, offers a solution to the challenges posed by limited expert trajectories and the data-sensitive characteristics of medical imaging. BaT features two interconnected elements: an asynchronous Stage Bank data pipeline that generates content-isolated training states independently of the policy-update loop, and BiCuRL (Bilevel Curriculum Reinforcement Learning), which is a self-improving method post-training. BiCuRL employs a fixed held-out evaluation to determine the next stage curriculum, assesses rollouts against task rubrics, updates the policy using GRPO, and sends the candidate checkpoint back for evaluation. This method tackles the issue of conventional post-training approaches that overlook stage-level rubrics, crucial for identifying failures. By utilizing these diagnostics, BaT facilitates ongoing enhancement without the need for extra expert data. The paper can be found on arXiv under the identifier 2608.16211.
Key facts
- BaT is a recursive self-improvement system for agent post-training.
- It targets long-horizon agents in medical imaging workflows.
- BaT includes Stage Bank data pipeline and BiCuRL method.
- Stage Bank synthesizes training states outside the policy-update loop.
- BiCuRL uses fixed held-out evaluation to select stage curriculum.
- BiCuRL verifies rollouts with task rubrics and updates policy with GRPO.
- The paper is available on arXiv with identifier 2608.16211.
- The approach addresses scarcity of expert trajectories in medical imaging.
Entities
Institutions
- arXiv