Apple researchers reveal that standard policy gradient algorithms unintentionally reduce exploration diversity in language models and propose actively managing entropy during training.

Policy gradient algorithms, which have advanced language model reasoning, naturally decrease entropy during training, limiting the diversity of explored trajectories and constraining a model's exploration capabilities

The reduction in entropy-driven exploration undermines a key strength of these algorithms: their ability to learn from diverse solutions generated through self-exploration

Researchers argue for active monitoring and control of entropy throughout the training process to preserve exploration diversity and foster more creative and varied solutions