Improving Data and Reward Design for Scientific Reasoning in Large Language Models

TL;DR

This paper introduces a comprehensive dataset and a novel training pipeline for scientific reasoning in large language models, significantly improving their ability to handle open-ended science questions.

Contribution

It presents the Dr. SCI dataset and a new post-training approach with three key components to enhance scientific reasoning capabilities.

Findings

Qwen3-4B-Base achieves 63.2 on GPQA-diamond

Model outperforms strong baselines like o1-mini and GPT-4o

Demonstrates substantial gains in open-ended scientific reasoning

Abstract

Solving open-ended science questions remains challenging for large language models, particularly due to inherently unreliable supervision and evaluation. The bottleneck lies in the data construction and reward design for scientific post-training. We develop a large-scale, systematic data processing pipeline that transforms heterogeneous open-source science data into Dr. SCI dataset, which comprises of 1M questions across eight STEM subjects, with explicit verifiable/open-ended splits, scalable difficulty annotation, and fine-grained rubrics that operationalize evaluation for open-ended answers. Building on this dataset, we propose the Dr. SCI post-training pipeline, which redesigns the standard SFT -> RL workflow through three components: (i) Exploration-Expanding SFT, which broadens the model's reasoning pattern coverage prior to RL; (ii) Dynamic Difficulty Curriculum, which adapts training data to the model's evolving scientific capability; and (iii) SciRubric-Guided RL, which enables stable reinforcement learning on open-ended scientific questions via rubric-based evaluation with explicit answer correctness. Qwen3-4B-Base trained using Dr. SCI pipeline achieves 63.2 on GPQA-diamond and 32.4 on GPQA-general, consistently improves over strong post-trained baselines such as o1-mini and GPT-4o, demonstrating substantial gains in scientific reasoning, especially in open-ended settings.