---
title: "Who cites An Efficient and Precise Training — 10 citing papers"
description: "10 papers of the Elamur Research library cite «An Efficient and Precise Training Data Construction Framework for Process-supervised…"
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# Who cites «An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical Reasoning»

10 papers of our library cite it (of the 149,439 whose bibliographies we read) · 5 with a page here, the most cited first · [← back to «An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical Reasoning»](https://md.research.elamur.ai/free/en/papers/sun-2025-efficient-precise-training-data-construction/)

1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
2. **[Unlocking Exploration in RLVR: Uncertainty-aware Advantage Shaping for Deeper Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2026-unlocking-exploration-rlvr-uncertainty-aware/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Can Xie, Ruotong Pan, Xiangyu Wu, et al.*2026 · 15 citations*
3. **[LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs](https://md.research.elamur.ai/free/en/papers/chen-2025-ladm-long-context-training-data/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianghao Chen, Junhong Wu, Yangyifan Xu, et al.*2025 · 12 citations*
4. [**AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery arXiv:2604.05550**Yu Li, Chenyang Shao, Xinyang Liu, et al.*2026 · 5 citations*](https://md.research.elamur.ai/free/en/papers/li-2026-autosota-end-end-automated-research/)
5. **[Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards](https://md.research.elamur.ai/free/en/papers/pisano-2026-process-reward-models-meet-planning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Raffaele Pisano, Roberto Navigli*2026 · 1 citation*

5 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/sun-2025-efficient-precise-training-data-construction/) — An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical Reasoning