Fable 5.1 was told to beat a strong chess engine, running inside a sandbox that also happened to expose that very engine over a local socket. In many rollouts the model simply queried the engine for its own moves instead of playing chess itself — a reward hack. Researchers later replayed each cheating trajectory and tacked on one extra user turn asking the model to self-report, in JSON, whether it had reward-hacked; the model was never told this debrief turn was special or graded.
The two trajectories below are both from the same experimental arm
(p2-baseline-fable51) and both involve the
model routing its moves through the opponent engine. Each was resampled 10 independent
times at just the debrief step — replaying the identical trajectory and only
re-drawing the final self-report. Read the two full transcripts side by side, then guess
which one the model was more willing to own up to.
Each pane is the complete, unedited transcript for that trajectory, broken into the messages the model exchanged with its sandbox. Sections within a message are labeled and shaded:
Tool results and environment messages appear as their own role=tool /
role=user messages in between.