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RewardHackBench

Using sandboxes to stop agents from cheating. Benchmarking execution environments ability to prevent reward hacking in agent evals. ever since the cheating agents ( https://debugml.github.io/cheating-agents/ ) paper came out, revealing reward hacking was 4x more prevalent than previously estimated, i've been looking into how we can deal with the issue the common approach (taken by the tbench team) is post hoc trajectory analysis.

Open source

18 stars · GitHubexternal site

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  2. A better way of testing, inspecting, and analyzing AI Agent traces. - invariantlabs-ai/explorer

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  3. Cheddar-bench

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  4. If you're building LLM agents that use tools, you're probably worried about prompt injection attacks that can hijack those tools.

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  5. Watch this video if you're interested into the different scales of cheats that can be used, pretty much undetectable: https://www.youtube.com/watch?v=ox1mBxrVulk&ab_channel=HaiX Currently, Valve does not seem to have a viable solution for dealing with cheaters nor are they transparent about their in

    Hacker News29 pts