# SCP-116K: A High-Quality Problem-Solution Dataset and a Generalized Pipeline for Automated Extraction in the Higher Education Science Domain

arXiv: https://arxiv.org/abs/2501.15587 · HTML: https://mlsanity.com/paper/2501.15587/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Placed in Mathematical reasoning, one of 17 directions in LLM reasoning.
- Builds on 7 of its 11 references that are in the map.
- First posted on arXiv 26 Jan 2025.

## Placed in

- LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/)

## Builds on (most cited first, 7 of 7)

- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://mlsanity.com/paper/2311.12022/) (2023)
- [Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering](https://mlsanity.com/paper/2209.09513/) (2022)
- [FrontierMath: A Benchmark for Evaluating Advanced Mathematical Reasoning in AI](https://mlsanity.com/paper/2411.04872/) (2024)
- [Imitate, Explore, and Self-Improve: A Reproduction Report on Slow-thinking Reasoning Systems](https://mlsanity.com/paper/2412.09413/) (2024)
- [MathBench: Evaluating the Theory and Application Proficiency of LLMs with a Hierarchical Mathematics Benchmark](https://mlsanity.com/paper/2405.12209/) (2024)
- [U-MATH: A University-Level Benchmark for Evaluating Mathematical Skills in LLMs](https://mlsanity.com/paper/2412.03205/) (2024)

## Built on by (most cited first, 30 of 31)

- [InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models](https://mlsanity.com/paper/2504.10479/) (2025)
- [InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency](https://mlsanity.com/paper/2508.18265/) (2025)
- [Rubrics as Rewards: Reinforcement Learning Beyond Verifiable Domains](https://mlsanity.com/paper/2507.17746/) (2025)
- [ProRL: Prolonged Reinforcement Learning Expands Reasoning Boundaries in Large Language Models](https://mlsanity.com/paper/2505.24864/) (2025)
- [A Survey of Reinforcement Learning for Large Reasoning Models](https://mlsanity.com/paper/2509.08827/) (2025)
- [Breaking the Exploration Bottleneck: Rubric-Scaffolded Reinforcement Learning for General LLM Reasoning](https://mlsanity.com/paper/2508.16949/) (2025)
- [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://mlsanity.com/paper/2507.16812/) (2025)
- [ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement](https://mlsanity.com/paper/2504.01934/) (2025)
- [SwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning](https://mlsanity.com/paper/2506.08989/) (2025)
- [Low-probability Tokens Sustain Exploration in Reinforcement Learning with Verifiable Reward](https://mlsanity.com/paper/2510.03222/) (2025)
- [ProRL Agent: Rollout-as-a-Service for RL Training of Multi-Turn LLM Agents](https://mlsanity.com/paper/2603.18815/) (2026)
- [Generative AI Act II: Test Time Scaling Drives Cognition Engineering](https://mlsanity.com/paper/2504.13828/) (2025)
- [Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs](https://mlsanity.com/paper/2506.14731/) (2025)
- [Golden Goose: A Simple Trick to Synthesize Unlimited RLVR Tasks from Unverifiable Internet Text](https://mlsanity.com/paper/2601.22975/) (2026)
- [WildSci: Advancing Scientific Reasoning from In-the-Wild Literature](https://mlsanity.com/paper/2601.05567/) (2026)
- [BlueLM-2.5-3B Technical Report](https://mlsanity.com/paper/2507.05934/) (2025)
- [Hala Technical Report: Building Arabic-Centric Instruction & Translation Models at Scale](https://mlsanity.com/paper/2509.14008/) (2025)
- [Segmental Advantage Estimation: Enhancing PPO for Long-Context LLM Training](https://mlsanity.com/paper/2601.07320/) (2026)
- [Deeper Thought, Weaker Aim: Understanding and Mitigating Perceptual Impairment during Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2603.14184/) (2026)
- [A Short Survey on Small Reasoning Models: Training, Inference, Applications and Research Directions](https://mlsanity.com/paper/2504.09100/) (2025)
- [FOCUS: Unified Vision-Language Modeling for Interactive Editing Driven by Referential Segmentation](https://mlsanity.com/paper/2506.16806/) (2025)
- [More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning](https://mlsanity.com/paper/2510.07169/) (2025)
- [Closing the Data Loop: Using OpenDataArena to Engineer Superior Training Datasets](https://mlsanity.com/paper/2601.09733/) (2025)
- [Every Coin Has Two Sides: On the Dual Nature of Generalization in On-Policy Distillation of Large Language Models](https://mlsanity.com/paper/2608.16647/) (2026)
- [When Can LLMs Learn to Reason with Weak Supervision?](https://mlsanity.com/paper/2604.18574/) (2026)
- [Scientific Logicality Enriched Methodology for LLM Reasoning: A Practice in Physics](https://mlsanity.com/paper/2605.17104/) (2026)
- [Decoupled Physical Modeling and Execution for Physics Reasoning](https://mlsanity.com/paper/2608.22126/) (2026)
- [Expert-validated STEM QA](https://mlsanity.com/paper/2608.28591/) (2026)
- [SPARK: Skeleton-Guided Reasoning Synthesis from Large-Scale Scientific Literature](https://mlsanity.com/paper/2608.30214/) (2026)
- [Collaborative Reasoning Distillation via Cross-Feedback and Coherent Curation](https://mlsanity.com/paper/2610.09587/) (2026)

## Abstract (arXiv)

Recent breakthroughs in large language models (LLMs) exemplified by the impressive mathematical and scientific reasoning capabilities of the o1 model have spotlighted the critical importance of high-quality training data in advancing LLM performance across STEM disciplines. While the mathematics community has benefited from a growing body of curated datasets, the scientific domain at the higher education level has long suffered from a scarcity of comparable resources. To address this gap, we present SCP-116K, a new large-scale dataset of 116,756 high-quality problem-solution pairs, automatically extracted from heterogeneous sources using a streamlined and highly generalizable pipeline. Our approach involves stringent filtering to ensure the scientific rigor and educational level of the extracted materials, while maintaining adaptability for future expansions or domain transfers. By openly releasing both the dataset and the extraction pipeline, we seek to foster research on scientific reasoning, enable comprehensive performance evaluations of new LLMs, and lower the barrier to replicating the successes of advanced models like o1 in the broader science community. We believe SCP-116K will serve as a critical resource, catalyzing progress in high-level scientific reasoning tasks and promoting further innovations in LLM development. The dataset and code are publicly available at https://github.com/AQA6666/SCP-116K-open.