Training Reasoning Models to Generate Diverse Scientific Ideas
To optimize for this, we propose training with a set-level reinforcement learning objective.
ProofPaper ↗
Key points
- One of the first steps of a research pipeline is generating ideas.
- In our work, we study document-grounded ideation, focusing on increasing the diversity of LLM generated ideas and ensuring they are high quality.
- In our setup, in a single generation, the model writes K structured idea cards grounded in the source papers, and the set is scored jointly, where each idea's quality is weighted by its distance from the ideas written before it.
- We show that our trained 8B model achieves higher creative utility (a measure combining quality and diversity) than Claude Opus 4.8 and GPT-6 Astra on our contamination-free benchmark.
Sources (1)
- [1]Training Reasoning Models to Generate Diverse Scientific IdeasarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 05:52 PM
To optimize for this, we propose training with a set-level reinforcement learning objective.
One of the first steps of a research pipeline is generating ideas.
Extractive summary: sentences quoted from the sources.
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