Scaling Discovery Requires Forgetting

What this work does

The authors find that longer or parallel agent searches plateau as accumulated histories anchor subsequent exploration. Periodically clearing search state while retaining the best candidate restores exploration and improves results on programming, kernel-optimization and model-training tasks.