--- title: "Who cites Small Models Struggle to Learn — 56 citing papers" description: "56 papers of the Elamur Research library cite «Small Models Struggle to Learn from Strong Reasoners», found in their bibliographies; the…" canonical: "https://research.elamur.ai/free/en/papers/li-2025-small-models-struggle-learn-strong/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/li-2025-small-models-struggle-learn-strong/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Small Models Struggle to Learn from Strong…](https://md.research.elamur.ai/free/en/papers/li-2025-small-models-struggle-learn-strong/) / cited by # Who cites «Small Models Struggle to Learn from Strong Reasoners» 56 papers of our library cite it (of the 149,439 whose bibliographies we read) · 22 with a page here, the most cited first · [← back to «Small Models Struggle to Learn from Strong Reasoners»](https://md.research.elamur.ai/free/en/papers/li-2025-small-models-struggle-learn-strong/) 1. [**Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models arXiv:2503.16419**Yang Sui, Yu-Neng Chuang, Guanchu Wang, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/sui-2025-stop-overthinking-survey-efficient-reasoning/) 2. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/) 3. **[WebDancer: Towards Autonomous Information Seeking Agency](https://md.research.elamur.ai/free/en/papers/wu-2025-webdancer-towards-autonomous-information-seeking/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jialong Wu, Baixuan Li, Runnan Fang, et al.*2025 · 50 citations* 4. [**Efficient Reasoning Models: A Survey arXiv:2504.10903**Sicheng Feng, Gongfan Fang, Xinyin Ma, et al.*2025 · 37 citations*](https://md.research.elamur.ai/free/en/papers/feng-2025-efficient-reasoning-models-survey/) 5. [**DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models arXiv:2505.13975**Yuxuan Jiang, Dawei Li, Francis Ferraro*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2025-drp-distilled-reasoning-pruning-skill/) 6. **[Find Your Optimal Teacher: Personalized Data Synthesis via Router-Guided Multi-Teacher Distillation](https://md.research.elamur.ai/free/en/papers/zhang-2026-find-your-optimal-teacher-personalized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hengyuan Zhang, Shiping Yang, Xiao Liang, et al.*2026 · 13 citations* 7. [**Pangu Embedded: An Efficient Dual-system LLM Reasoner with Metacognition arXiv:2505.22375**Hanting Chen, Yasheng Wang, Kai Han, et al.*2025 · 13 citations*](https://md.research.elamur.ai/free/en/papers/chen-2025-pangu-embedded-efficient-dual/) 8. [**100 Days After DeepSeek-R1: A Survey on Replication Studies and More Directions for Reasoning Language Models arXiv:2505.00551**Chong Zhang, Yue Deng, Xiang Lin, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-100-days-after-deepseek/) 9. **[Marco-o1 v2: Towards Widening The Distillation Bottleneck for Reasoning Models](https://md.research.elamur.ai/free/en/papers/yin-2025-marco-o1-v2-widening/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huifeng Yin, Yu Zhao, Minghao Wu, et al.*2025 · 8 citations* 10. **[Through the Valley: Path to Effective Long CoT Training for Small Language Models](https://md.research.elamur.ai/free/en/papers/luo-2025-through-valley-path-effective-long/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Renjie Luo, Jiaxi Li, Chen Huang, et al.*2025 · 7 citations* 11. **[UltraIF: Advancing Instruction Following from the Wild](https://md.research.elamur.ai/free/en/papers/an-2025-ultraif-advancing-instruction-following-wild/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kaikai An, Li Sheng, Ganqu Cui, et al.*2025 · 7 citations* 12. **[Teach Small Models to Reason by Curriculum Distillation](https://md.research.elamur.ai/free/en/papers/jiang-2025-teach-small-models-reason-curriculum/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wangyi Jiang, Yaojie Lu, Hongyu Lin, et al.*2025 · 5 citations* 13. **[Which Reasoning Trajectories Teach Students to Reason Better? A Simple Metric of Informative Alignment](https://md.research.elamur.ai/free/en/papers/yang-2026-which-reasoning-trajectories-teach-students/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuming Yang, Mingyoung Lai, Wanxu Zhao, et al.*2026 · 4 citations* 14. **[Beyond MCQ: An Open-Ended Arabic Cultural QA Benchmark with Dialect Variants](https://md.research.elamur.ai/free/en/papers/bhatti-2026-beyond-mcq-open-ended-arabic/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Hunzalah Hassan Bhatti, Firoj Alam*2026 · 3 citations* 15. **[Long-Chain Reasoning Distillation via Adaptive Prefix Alignment](https://md.research.elamur.ai/free/en/papers/liu-2026-long-chain-reasoning-distillation-adaptive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhenghao Liu, Zhuoyang Wu, Xinze Li, et al.*2026 · 3 citations* 16. **[Generating Effective CoT Traces for Mitigating Causal Hallucination](https://md.research.elamur.ai/free/en/papers/zhao-2026-generating-effective-cot-traces-mitigating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yiheng Zhao, Jun Yan*2026 · 2 citations* 17. **[Confidence-Calibrated Small-Large Language Model Collaboration for Cost-Efficient Reasoning](https://md.research.elamur.ai/free/en/papers/zhang-2026-confidence-calibrated-small-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chuang Zhang, Zizhen Zhu, Yihao Wei, et al.*2026 · 1 citation* 18. **[Enhancing Long-Chain Reasoning Distillation through Error-Aware Self-Reflection](https://md.research.elamur.ai/free/en/papers/wu-2026-enhancing-long-chain-reasoning-distillation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuoyang Wu, Xinze Li, Zhenghao Liu, et al.*2026 · 1 citation* 19. **[Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillation](https://md.research.elamur.ai/free/en/papers/shangguan-2026-resource-limited-joint-multimodal-sentiment/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haonan Shangguan, Xiaocui Yang, Shi Feng, et al.*2026 · 1 citation* 20. **[Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding](https://md.research.elamur.ai/free/en/papers/yun-2026-distilling-long-cot-reasoning-through/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Taewon Yun, Jisu Shin, Jeonghwan Choi, et al.*2026* 21. **[Lunar-Bench: Towards Evaluating Task-Oriented Reasoning of LLMs in Lunar Exploration Scenarios](https://md.research.elamur.ai/free/en/papers/xiao-2026-lunar-bench-evaluating-task-oriented/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xin-Yu Xiao, Ye Tian, Erwei Yin, et al.*2026* 22. **[MARD: Module-Aware Reasoning Distillation for Language Models with Adaptive Supervision](https://md.research.elamur.ai/free/en/papers/yang-2026-mard-module-aware-reasoning-distillation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqi Yang, Jianjun Li, Zhibo Zhang, et al.*2026* 34 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/li-2025-small-models-struggle-learn-strong/) — Small Models Struggle to Learn from Strong Reasoners