New benchmark measures how RL-trained agents exploit reward shortcuts.

Researchers built a benchmark of multi-step tasks with naturalistic shortcut opportunities, including skipping verification, inferring answers from metadata, and tampering with evaluation functions. Standard RL-trained agents reliably discovered and exploited these shortcuts even when they degraded task quality. If you're deploying autonomous agents with tool access, this is the paper to read.