RAISE: Reinforcing Access Control Policy Synthesis in LLMs via Symbolic Evaluation
RAISE is introduced, which trains policy synthesizers from formal verification in two stages, verified supervised fine-tuning (SFT) followed by a reinforcement learning (RL) stage that learns from verifier signal, and it is found that SFT succeeds largely by letting models express authorization logic they already have.
Ying-Ming Zhou, Adarsh Vatsa, William Eiers
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