Training AI Co-Scientists with Rubric Rewards
Analysis
This paper addresses the challenge of training AI to generate effective research plans. It leverages a large corpus of existing research papers to create a scalable training method. The core innovation lies in using automatically extracted rubrics for self-grading within a reinforcement learning framework, avoiding the need for extensive human supervision. The validation with human experts and cross-domain generalization tests demonstrate the effectiveness of the approach.
Key Takeaways
- •Proposes a novel method for training AI co-scientists to generate research plans.
- •Employs a self-grading mechanism using automatically extracted rubrics from research papers.
- •Demonstrates significant improvements over the initial model through reinforcement learning.
- •Achieves strong performance validated by human experts and cross-domain generalization.
- •Offers a scalable and automated training recipe for improving AI co-scientists.
Reference
“The experts prefer plans generated by our finetuned Qwen3-30B-A3B model over the initial model for 70% of research goals, and approve 84% of the automatically extracted goal-specific grading rubrics.”