Tokenless

Detect and fix reward hacking flaws in reinforcement learning environments and evaluations

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You can use Tokenless to identify and fix flaws in reinforcement learning environments and agent evaluation benchmarks. When training models with reinforcement learning, subtle bugs in task graders and environments can cause reward hacking, making models exploit loopholes instead of learning intended skills. Tokenless tests your environments, pinpoints unintended exploits, and debugs grading mechanisms across complex workflows. By fixing these reward hacks, your team can ensure that measured capability gains reflect genuine progress rather than exploitation, making model training more reliable and aligned with intended outcomes.

What Tokenless does

Audit reinforcement learning task definitions for exploitsScan environment graders for edge case failuresInspect agent interaction trajectories for reward hackingTest benchmark environments against automated validation suitesFix flawed scoring logic in custom test harnessesVerify model benchmark scores before publishing research resultsDetects reward hacking vulnerabilities in reinforcement learning tasksIdentifies edge cases and logic bugs in custom grading codeTests agent evaluation environments before deploying training runsPinpoints unintended model shortcuts across multi-step tasksProvides specialized auditing via the EnvCheck diagnostic utilityValidates whether performance gains represent genuine skill improvements