--- title: "Who cites HS-STaR: Hierarchical Sampling — 8 citing papers" description: "8 papers of the Elamur Research library cite «HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget…" canonical: "https://research.elamur.ai/free/en/papers/xiong-2025-hs-star-hierarchical-sampling-self/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/xiong-2025-hs-star-hierarchical-sampling-self/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [HS-STaR: Hierarchical Sampling for Self-Taught…](https://md.research.elamur.ai/free/en/papers/xiong-2025-hs-star-hierarchical-sampling-self/) / cited by # Who cites «HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation» 8 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 «HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation»](https://md.research.elamur.ai/free/en/papers/xiong-2025-hs-star-hierarchical-sampling-self/) 1. [**How Is LLM Reasoning Distracted by Irrelevant Context? An Analysis Using a Controlled Benchmark**Minglai Yang, Ethan Huang, Liang Zhang, et al.*2025 · 25 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-llm-reasoning-distracted-irrelevant-context/) 2. **[POSITION BIAS MITIGATES POSITION BIAS: Mitigate Position Bias Through Inter-Position Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/wang-2025-position-bias-mitigates-position-bias/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yifei Wang, Feng Xiong, Yong Wang, et al.*2025 · 13 citations* 6 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/xiong-2025-hs-star-hierarchical-sampling-self/) — HS-STaR: Hierarchical Sampling for Self-Taught Reasoners via Difficulty Estimation and Budget Reallocation