Pretrained language models are commonly adapted to comply with human intent and downstream tasks via finetuning. The finetuning process involves supervised finetuning (SFT), using labeled samples, and/or reinforcement learning based fine-tuning (RFT) via policy gradient methods, using a (possibly learned) reward function. This work highlights an overlooked optimization hurdle in RFT: we prove that the expected gradient for an input sample (i.e. prompt) vanishes if its reward standard deviation under the model is low, regardless of whether the reward mean is near-optimal or not. We then…Apple Machine Learning Research