Risk-Aware Self-Play Reinforcement Learning for Secure and Capable Code-Generating Agents
This paper investigates risk-aware self-play reinforcement learning for code-generating agents, demonstrating that while verifier-aware rewards significantly reduce security violations compared to public-test-only approaches, the method fails to establish a consistent self-play advantage or formal deployment guarantees due to limitations in safety interpretation and transferability.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
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