--- title: "Who cites Scaling Test-Time Compute — 26 citing papers" description: "26 papers of the Elamur Research library cite «Scaling Test-Time Compute Without Verification or RL is Suboptimal», found in their…" canonical: "https://research.elamur.ai/free/en/papers/setlur-2025-scaling-test-time-compute-verification/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/setlur-2025-scaling-test-time-compute-verification/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Scaling Test-Time Compute Without Verification or…](https://md.research.elamur.ai/free/en/papers/setlur-2025-scaling-test-time-compute-verification/) / cited by # Who cites «Scaling Test-Time Compute Without Verification or RL is Suboptimal» 26 papers of our library cite it (of the 149,439 whose bibliographies we read) · 10 with a page here, the most cited first · [← back to «Scaling Test-Time Compute Without Verification or RL is Suboptimal»](https://md.research.elamur.ai/free/en/papers/setlur-2025-scaling-test-time-compute-verification/) 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. **[Learning to Reason under Off-Policy Guidance](https://md.research.elamur.ai/free/en/papers/yan-2025-learning-reason-under-off-policy/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jianhao Yan, Yafu Li, Zican Hu, et al.*2025 · 68 citations* 3. **[The Unreasonable Effectiveness of Entropy Minimization in LLM Reasoning](https://md.research.elamur.ai/free/en/papers/agarwal-2025-unreasonable-effectiveness-entropy-minimization-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Agarwal, Zimin Zhang, Lifan Yuan, et al.*2025 · 47 citations* 4. [**Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models arXiv:2502.17387**Alon Albalak, Duy Phung, Nathan Lile, et al.*2025 · 43 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2025-big-math-large-scale-high/) 5. [**OpenCodeReasoning: Advancing Data Distillation for Competitive Coding arXiv:2504.01943**Wasi Uddin Ahmad, Sean Narenthiran, Somshubra Majumdar, et al.*2025 · 33 citations*](https://md.research.elamur.ai/free/en/papers/ahmad-2025-opencodereasoning-advancing-data-distillation-competitive/) 6. [**Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models arXiv:2503.24377**Rui Wang, Hongru Wang, Boyang Xue, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/) 7. [**When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning arXiv:2504.01005**Nishad Singhi, Hritik Bansal, Arian Hosseini, et al.*2025 · 12 citations*](https://md.research.elamur.ai/free/en/papers/singhi-2025-solve-verify-compute-optimal-problem/) 8. [**Making Small Language Models Efficient Reasoners: Intervention, Supervision, Reinforcement arXiv:2505.07961**Xuechen Zhang, Zijian Huang, Chenshun Ni, et al.*2025 · 6 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-making-small-language-models-efficient/) 9. **[Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment](https://md.research.elamur.ai/free/en/papers/zhang-2026-generalizable-llm-learning-graph-synthetic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yizhuo Zhang, Heng Wang, Shangbin Feng, et al.*2026* 10. **[LoVeC: Reinforcement Learning for Better Verbalized Confidence in Long-Form Generation](https://md.research.elamur.ai/free/en/papers/zhang-2026-lovec-reinforcement-learning-better-verbalized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Caiqi Zhang, Xiaochen Zhu, Chengzu Li, et al.*2026* 16 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/setlur-2025-scaling-test-time-compute-verification/) — Scaling Test-Time Compute Without Verification or RL is Suboptimal