arXiv:2606.11521v1 Announce Type: new Abstract: LLMs and LLM agents should improve when given feedback, but identifying when they are able to do so is difficult: feedback is heterogeneous, domain-specific, and difficult to control. We approach this challenge by asking LLMs to perform regular-expression induction, a classical symbolic learning problem where precise mechanisms for feedback exist in the form of counterexamples. In counterexample-guided learning, a learner (LLM) proposes candidate regular expressions from positive/negative-labeled strings, and the teacher (verifier) returns counte
The increasing sophistication of LLMs and LLM agents necessitates more robust and scalable feedback mechanisms to improve their performance and reliability, moving beyond manual feedback loops.
Improving how LLMs learn from feedback, especially through structured reasoning like counterexamples, is critical for developing more capable and autonomous AI agents that can tackle complex real-world problems.
This research introduces a scalable method for guided learning in LLMs, allowing them to systematically refine their outputs based on precise, automated feedback, rather than relying solely on large, uncurated datasets or human oversight.
- · AI model developers
- · Autonomous agent builders
- · Academic AI research
- · Enterprises adopting AI agents
- · Tasks requiring manual iterative AI refinement
- · AI development relying solely on prompt engineering
AI agents will exhibit improved performance and reliability in tasks requiring logical inference and pattern recognition.
This improved reliability accelerates the deployment and integration of AI agents into complex operational workflows, collapsing existing white-collar tasks.
The ability of AI to learn more effectively from structured feedback could lead to more robust autonomous systems in sensitive domains like finance or defense, impacting trust and regulatory frameworks.
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Read at arXiv cs.LG