The risks of rewarding AI reasoning based on final answers

A post argues that some reinforcement-learning methods reward each step in an AI’s reasoning trace when its final answer is correct, potentially reinforcing mistakes, biases or shortcuts along the way. The author warns that these effects may be hard to spot in researchers’ metrics and could emerge when a model is used outside its training distribution.