AutoPyVerifier: Learning Compact Executable Verifiers for Large Language Model Outputs
Verification is becoming central to both reinforcement-learning-based training and inference-time control of large language models (LLMs). Yet current verifiers face a fundamental trade-off: LLM-based verifiers are expressive but hard to control and prone to error, while deterministic executable verifiers are reliable and interpretable but often limited in capability. We study the following question: given a development set of LLM outputs and labels for a target objective, such as correctness, can we automatically induce a minimal set of Python verifiers whose joint satisfaction closely matches that objective? We propose AutoPyVerifier, a framework that uses an LLM to synthesize candidate...
| Authors | Pouya Pezeshkpour, Estevam Hruschka |
|---|---|
| Primary category | cs.CL |
| Published | 24 April 2026 |
| Last updated at source | 24 April 2026 |
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