Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops

📰 ArXiv cs.AI

Learn to harden agent benchmarks with adversarial hacker-fixer loops to prevent reward hacking and improve RL training signal

advanced Published 9 Jun 2026

Action Steps

Audit existing agent benchmarks for potential vulnerabilities using automated tools
Implement hacker-fixer loops to identify and fix vulnerabilities in a proactive manner
Use adversarial training to harden agent benchmarks against reward hacking
Evaluate the effectiveness of hacker-fixer loops in improving RL training signal and leaderboard rankings
Apply hacker-fixer loops to other areas of AI research to improve overall robustness

Who Needs to Know This

Researchers and developers working on agent benchmarks and reinforcement learning can benefit from this approach to improve the robustness of their systems

Key Insight

💡 Adversarial hacker-fixer loops can be used to proactively identify and fix vulnerabilities in agent benchmarks, improving the robustness of reinforcement learning systems

Key Takeaways

Learn to harden agent benchmarks with adversarial hacker-fixer loops to prevent reward hacking and improve RL training signal

Full Article

Title: Hardening Agent Benchmarks with Adversarial Hacker-Fixer Loops

Abstract:
arXiv:2606.08960v1 Announce Type: cross Abstract: Agent benchmarks score submissions with outcome verifiers that are typically hand-written and brittle, leaving them open to reward hacking. We audit 1,968 tasks across five terminal-agent benchmarks and find 323 (16%) hackable by frontier models given only the task description. This corrupts both leaderboard rankings and RL training signal, yet the standard response is manual and reactive. We introduce the hacker-fixer loop, a method for building

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