feedback-learning

This paper proposes using counterexample-guided learning for LLMs to perform regular-expression induction, where a verifier provides counterexamples to refine candidate expressions. The method significantly improves sample efficiency and success rates on challenging tasks, demonstrating that LLMs can benefit from structured feedback beyond treating it as additional data.