🔗 Who cites «Self-Evolving Curriculum for LLM Reasoning»

20 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «Self-Evolving Curriculum for LLM Reasoning»

1. Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM ReasoningarXiv:2506.06632Shubham Parashar, Shurui Gui, Xiner Li, et al.2025 · 23 citations
2. Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical ReasoningFindings of the Association for Computational LinguisticsJun Rao, Xuebo Liu, Hexuan Deng, et al.2026 · 4 citations
3. 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
4. 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
5. SCALER: Synthetic Scalable Adaptive Learning Environment for ReasoningFindings of the Association for Computational LinguisticsCaijun Xu, Changyi Xiao, Zhongyuan Peng, et al.2026 · 2 citations
6. SPARD: Self-Paced Curriculum for RL Alignment via Integrating Reward Dynamics and Data UtilityAnnual Meeting of the Association for Computational Linguistics (ACL)Xuyang Zhi, Peilun Zhou, Chengqiang Lu, et al.2026 · 1 citation
7. Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-TuningFindings of the Association for Computational LinguisticsMu Zhang, Yuxiang Chu, Guangya Yu, et al.2026
8. DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning CollectionsFindings of the Association for Computational LinguisticsHaebin Shin, Lei Ji, Xiao Liu, et al.2026

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

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