--- title: "Who cites Enhancing the Reasoning — 7 citing papers" description: "7 papers of the Elamur Research library cite «Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance…" canonical: "https://research.elamur.ai/free/en/papers/bi-2025-enhancing-reasoning-capabilities-small-language/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/bi-2025-enhancing-reasoning-capabilities-small-language/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Enhancing the Reasoning Capabilities of Small…](https://md.research.elamur.ai/free/en/papers/bi-2025-enhancing-reasoning-capabilities-small-language/) / cited by # Who cites «Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning» 7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning»](https://md.research.elamur.ai/free/en/papers/bi-2025-enhancing-reasoning-capabilities-small-language/) 1. [**Guiding Reasoning in Small Language Models with LLM Assistance arXiv:2504.09923**Yujin Kim, Euiin Yi, Minu Kim, et al.*2025 · 9 citations*](https://md.research.elamur.ai/free/en/papers/kim-2025-guiding-reasoning-small-language-models/) 2. **[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* 5 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/bi-2025-enhancing-reasoning-capabilities-small-language/) — Enhancing the Reasoning Capabilities of Small Language Models via Solution Guidance Fine-Tuning