🔗 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»

1. A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super IntelligencearXiv:2507.21046Huan-ang Gao, Jiayi Geng, Wenyue Hua, et al.2025 · 36 citations
2. What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Yaning Jia, Chunhui Zhang, Xingjian Diao, et al.2026 · 4 citations
3. Imbalanced Gradients in RL Post-Training of Multi-Task LLMsFindings of the Association for Computational LinguisticsRunzhe Wu, Ankur Samanta, Ayush Jain, et al.2026 · 3 citations
4. SCALER: Synthetic Scalable Adaptive Learning Environment for ReasoningFindings of the Association for Computational LinguisticsCaijun Xu, Changyi Xiao, Zhongyuan Peng, et al.2026 · 2 citations
5. Chart-RL: Generalized Chart Comprehension via Reinforcement Learning with Verifiable RewardsWorkshop on Structured Understanding, Retrieval, and Generation in the LLM EraXin Zhang, Xingyu Li, Rongguang Wang, et al.2026 · 1 citation

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/parashar-2025-curriculum-reinforcement-learning-easy-hard/cited-by/