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Zhibin Kang

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Preprint Jul 2026

Evaluating Fuzz Testing for Reinforcement Learning Agents

It is shown that fuzzing-generated crashes can meaningfully improve agent robustness and enable accurate safety monitoring with strong cross-method generalization, and the benefits of combining complementary fuzzing strategies and adopting multi-level diversity analysis to achieve more comprehensive and practical RL testing.

Zhibin Kang, Hanmo You, Dong Wang et al. · 0 citations