LDC: Learning to Generate Research Idea with Dynamic Control

Understand

Recent advancements in large language models (LLMs) have demonstrated their potential in automating the scientific research ideation.

• Existing approaches primarily focus on prompting techniques, often producing ideas misaligned with expert standards - novelty, feasibility, and effectiveness, which are widely recognized by the research community as the three key subdimensions of high-quality ideas.
• Also, balancing these dimensions remains challenging due to their inherent trade-offs.
• To address these limitations, we propose the first framework that employs a two-stage approach combining Supervised Fine-Tuning (SFT) and controllable Reinforcement Learning (RL) for the task.