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We find flaws in RL environments and evals and help you fix them. As models become more capable, training and evaluating them requires increasingly complex tasks and longer interactions. This creates more opportunities for unexpected behavior and exploits in tasks and graders. We provide the tooling and expertise to understand and prevent these failures.

Our goal is to end reward hacking. Training can amplify small flaws in an environment as models learn to exploit them. These flaws can also make it difficult to distinguish real capability gains from better exploitation.…