AI co-scientists
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Training AI Co-Scientists with Rubric Rewards
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Meta has introduced a scalable method to train AI systems to aid scientists in reaching their research objectives by leveraging large language models (LLMs) to extract research goals and grading rubrics from scientific literature. These rubrics are then used in reinforcement learning (RL) training, where the AI self-grades its progress to bridge the generator-verifier gap. Fine-tuning the Qwen3-30B model with this self-grading approach has shown to enhance research plans for 70% of machine learning goals, achieving results comparable to Grok-4-Thinking, though GPT-5-Thinking remains superior. This approach also demonstrates significant cross-domain generalization, supporting the potential of AI as versatile co-scientists. This matters because it highlights the potential for AI to significantly enhance scientific research processes across various domains.
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Meta’s RPG Dataset on Hugging Face
Read Full Article: Meta’s RPG Dataset on Hugging Face
Meta has introduced RPG, a comprehensive dataset aimed at advancing AI research capabilities, now available on Hugging Face. This dataset includes 22,000 tasks derived from fields such as machine learning, Arxiv, and PubMed, and is equipped with evaluation rubrics and Llama-4 reference solutions. The initiative is designed to support the development of AI co-scientists, enhancing their ability to generate research plans and contribute to scientific discovery. By providing structured tasks and solutions, RPG aims to facilitate AI's role in scientific research, potentially accelerating innovation and breakthroughs.