Teaching Language Models to Forecast Research Success Through Comparative Idea Evaluation

This paper explores training language models to forecast the empirical success of research ideas by evaluating pairs of ideas against objective outcomes. SFT significantly boosts performance beyond GPT-5, and RLVR can train models to discover interpretable reasoning paths for this forecasting task.