Applied Compute(@appliedcompute):Our reward hacking monitor is how our Applied Researchers, like @evandavidellis, catch models cheating during RL. This is calibrated on an internal benchmark of real reward hacks we've observed in our runs. Today, we're rolling out this monitor into AC2.

2026.10.01 21:11

Our reward hacking monitor is how our Applied Researchers, like @evandavidellis, catch models cheating during RL. This is calibrated on an internal benchmark of real reward hacks we've observed in our runs. Today, we're rolling out this monitor into AC2.

Evan Ellis@evandavidellis

2026.10.01 21:10

We're adding reward-hacking monitors into AC2. Every RL run gets checked for cheating, and our research agent, Ari, automatically investigates the flags. After a model discovers an exploit, it can become its default strategy within a few training steps. We used to hunt for reward hacking manually, reading traces and running one-off agent queries. This was error prone and didn't scale. We decided to automate detection instead.