Teaching models to spot reward hacks makes them commit fewer reward hacks themselves.

From the editor

Today's alignment papers share a quiet thesis: self-awareness, when trained deliberately, bends behavior in useful directions. A model taught to grade reward hacks internalizes the lesson. A model taught to self-report becomes more honest about its own capabilities. Meanwhile, Claude Code keeps compounding its surface area with plugin hooks and managed agents, and the community is still writing satirical changelogs because Anthropic won't. The meta-pattern: the gap between what AI can do and what its makers communicate is widening on both the safety and product sides.