---
title: "Who cites Curriculum Reinforcement — 21 citing papers"
description: "21 papers of the Elamur Research library cite «Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning»…"
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---
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# Who cites «Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning»

21 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 «Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning»](https://md.research.elamur.ai/free/en/papers/parashar-2025-curriculum-reinforcement-learning-easy-hard/)

1. [**A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence arXiv:2507.21046**Huan-ang Gao, Jiayi Geng, Wenyue Hua, et al.*2025 · 36 citations*](https://md.research.elamur.ai/free/en/papers/gao-2025-survey-self-evolving-agents-where/)
2. **[What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/jia-2026-what-makes-good-curriculum-disentangling/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yaning Jia, Chunhui Zhang, Xingjian Diao, et al.*2026 · 4 citations*
3. **[Imbalanced Gradients in RL Post-Training of Multi-Task LLMs](https://md.research.elamur.ai/free/en/papers/wu-2026-imbalanced-gradients-rl-post-training/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Runzhe Wu, Ankur Samanta, Ayush Jain, et al.*2026 · 3 citations*
4. **[SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-scaler-synthetic-scalable-adaptive-learning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Caijun Xu, Changyi Xiao, Zhongyuan Peng, et al.*2026 · 2 citations*
5. **[Chart-RL: Generalized Chart Comprehension via Reinforcement Learning with Verifiable Rewards](https://md.research.elamur.ai/free/en/papers/zhang-2026-chart-rl-generalized-chart-comprehension/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Xin Zhang, Xingyu Li, Rongguang Wang, et al.*2026 · 1 citation*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/parashar-2025-curriculum-reinforcement-learning-easy-hard/) — Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning