Training Reasoning Models to Generate Diverse Scientific Ideas

Training with a set-level reinforcement learning objective optimizes LLMs to generate K structured, document-grounded idea cards that are both high-quality and distinct. This approach shifts optimization from individual idea quality to the joint diversity of the entire generated set, addressing the tendency of current models to produce redundant outputs.