arXiv:2605.30914v1 Announce Type: new Abstract: Automated formal verification remains challenging for large language models because data for proof assistants and verification-aware languages is scarce, and correctness depends on satisfying precise machine-checkable specifications rather than producing plausible code. This thesis studies how verifier environments can improve LLM generation of verified programs and proofs through reinforcement learning from verifiable rewards (RLVR) and verifier-guided inference-time search. First, we train open-source models in Dafny with RLVR using Group Relat
The scarcity of high-quality data for formal verification and the inherent difficulty for LLMs to generate precisely correct, machine-checkable specifications necessitates new approaches like reinforcement learning and verifier-guided inference.
This breakthrough addresses a fundamental limitation of current LLMs by improving their ability to produce provably correct code, which is crucial for safety-critical systems and advancing autonomous agent capabilities.
The development of automated formal verification using reinforcement learning and recursive inference marks a significant step towards more reliable and trustworthy AI-generated software and systems.
- · AI agents developers
- · Cybersecurity sector
- · Aerospace and defence companies
- · Software quality assurance
- · Manual formal verification services
- · Traditional software debugging methods
LLMs gain a new capability to generate verifiable code and proofs, reducing errors and increasing trust in AI-produced software.
The improved reliability of AI-generated code will accelerate the deployment of autonomous systems in sensitive or critical applications.
This could lead to a self-improving loop where AI not only generates code but also formally verifies its own creations, enhancing overall system robustness and autonomy.
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Read at arXiv cs.LG