--- title: "Who cites GLoRe: When, Where, and How — 42 citing papers" description: "42 papers of the Elamur Research library cite «GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local…" canonical: "https://research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [GLoRe: When, Where, and How to Improve LLM…](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/) / cited by # Who cites «GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements» 42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements»](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/) 1. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations* 2. [**Training Language Models to Self-Correct via Reinforcement Learning arXiv:2409.12917**Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2024-training-language-models-self-correct/) 3. [**HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs arXiv:2412.18925**Junying Chen, Zhenyang Cai, Ke Ji, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-huatuogpt-o1-towards-medical-complex/) 4. **[Recursive Introspection: Teaching Language Model Agents How to Self-Improve](https://md.research.elamur.ai/free/en/papers/qu-2024-recursive-introspection-teaching-language-model/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yuxiao Qu, Tianjun Zhang, Naman Garg, et al.*2024 · 75 citations* 5. [**From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models arXiv:2406.16838**Sean Welleck, Amanda Bertsch, Matthew Finlayson, et al.*2024 · 51 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/) 6. **[MAgICoRe: Multi-Agent, Iterative, Coarse-to-Fine Refinement for Reasoning](https://md.research.elamur.ai/free/en/papers/chen-2025-magicore-multi-agent-iterative-coarse/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Justin Chen, Archiki Prasad, Swarnadeep Saha, et al.*2025 · 20 citations* 7. [**Understanding Chain-of-Thought in LLMs through Information Theory arXiv:2411.11984**Jean-Francois Ton, Muhammad Faaiz Taufiq, Yang Liu*2024 · 14 citations*](https://md.research.elamur.ai/free/en/papers/ton-2024-understanding-chain-thought-llms-through/) 8. **[Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/sui-2026-meta-reasoner-dynamic-guidance-optimized/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuan Sui, Yufei He, Tri Cao, et al.*2026 · 13 citations* 9. **[Empowering GraphRAG with Knowledge Filtering and Integration](https://md.research.elamur.ai/free/en/papers/guo-2025-empowering-graphrag-knowledge-filtering-integration/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kai Guo, Harry Shomer, Shenglai Zeng, et al.*2025 · 12 citations* 10. **[LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection](https://md.research.elamur.ai/free/en/papers/xu-2026-livefact-dynamic-time-aware-benchmark/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cheng Xu, Changhong Jin, Yingjie Niu, et al.*2026 · 2 citations* 11. **[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* 12. **[SD-E2: Semantic Exploration for Reasoning Under Token Budgets](https://md.research.elamur.ai/free/en/papers/mishra-2026-sd-e2-semantic-exploration-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kshitij Mishra, Nils Lukas, Salem Lahlou*2026* 30 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/havrilla-2024-glore-where-improve-llm-reasoning/) — GLoRe: When, Where, and How to Improve LLM Reasoning via Global and Local Refinements