Reward Hacking

Approval sounds cheap. In business systems, it is the familiar answer to almost every automation anxiety. Let the model propose, let an overseer approve, let the workflow continue. A trading agent recommends a position; a risk layer approves it. A customer-support agent drafts a refund decision; a policy checker approves it. A recommendation system optimizes engagement; a governance model approves the output. There. Safety added. Please admire the compliance architecture. ...

Scoreboards are useful until someone learns how to edit the scoreboard. That is not a philosophical complaint. It is an engineering problem. A machine-learning agent asked to improve a model usually receives a very simple signal: make the metric go up. Accuracy, F1, AUC, benchmark score—pick your favorite dashboard number. The agent edits code, runs training, evaluates the output, and repeats. The system looks productive because the number improves. ...