--- title: "Mathematics & statistics — papers, page 3" description: "Mathematics & statistics: 161 papers of the Elamur Research library · page 3 of 7 — our summary of each, its ideas, references and who cites it." canonical: "https://research.elamur.ai/free/en/shelves/mathematics/page/3/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/shelves/mathematics/page/3/) [home](https://md.research.elamur.ai/free/en/) / [shelves](https://md.research.elamur.ai/free/en/shelves/) / [Mathematics & statistics](https://md.research.elamur.ai/free/en/shelves/mathematics/) / page 3 # ➗ Mathematics & statistics 161 papers · page 3 of 7 · newest first · [all shelves](https://md.research.elamur.ai/free/en/shelves/) Mathematics, proofs, computational mathematics, word problems and how machines learn to solve them, statistics. [← prev](https://md.research.elamur.ai/free/en/shelves/mathematics/page/2/) [1](https://md.research.elamur.ai/free/en/shelves/mathematics/) [2](https://md.research.elamur.ai/free/en/shelves/mathematics/page/2/) **3** [4](https://md.research.elamur.ai/free/en/shelves/mathematics/page/4/) … [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/4/) - [Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters](https://md.research.elamur.ai/free/en/papers/snell-2024-scaling-llm-test-time-compute/) — Charlie Snell, Jaehoon Lee, Kelvin Xu, et al. · 2024 - [Scaling Synthetic Data Creation with 1,000,000,000 Personas](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/) — Tao Ge, Xin Chan, Xiaoyang Wang, et al. · 2024 - [Solving olympiad geometry without human demonstrations](https://md.research.elamur.ai/free/en/papers/trinh-2024-solving-olympiad-geometry-without-human/) — Trieu H. Trinh, Yuhuai Wu, Quoc V. Le, et al. · 2024 - [Step-DPO: Step-wise Preference Optimization for Long-chain Reasoning of LLMs](https://md.research.elamur.ai/free/en/papers/lai-2024-step-dpo-step-wise-preference/) — Xin Lai, Zhuotao Tian, Yukang Chen, et al. · 2024 - [The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/) — Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al. · 2024 - [TinyLlama: An Open-Source Small Language Model](https://md.research.elamur.ai/free/en/papers/zhang-2024-tinyllama-open-source-small-language/) — Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al. · 2024 - [Training Language Models to Self-Correct via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/kumar-2024-training-language-models-self-correct/) — Aviral Kumar, Vincent Zhuang, Rishabh Agarwal, et al. · 2024 - [Tulu 3: Pushing Frontiers in Open Language Model Post-Training](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/) — Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al. · 2024 - [V-STaR: Training Verifiers for Self-Taught Reasoners](https://md.research.elamur.ai/free/en/papers/hosseini-2024-v-star-training-verifiers-self/) — Arian Hosseini, Xingdi Yuan, Nikolay Malkin, et al. · 2024 - [Are Emergent Abilities of Large Language Models a Mirage?](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-emergent-abilities-large-language-models/) — Rylan Schaeffer, Brando Miranda, Sanmi Koyejo · 2023 - [Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models](https://md.research.elamur.ai/free/en/papers/singh-2023-human-data-scaling-self-training/) — Avi Singh, John D. Co-Reyes, Rishabh Agarwal, et al. · 2023 - [C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models](https://md.research.elamur.ai/free/en/papers/huang-2023-c-eval-multi-level-multi/) — Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, et al. · 2023 - [Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them](https://md.research.elamur.ai/free/en/papers/suzgun-2023-challenging-big-bench-tasks-whether/) — Mirac Suzgun, Nathan Scales, Nathanael Schärli, et al. · 2023 - [CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/) — Zhibin Gou, Zhihong Shao, Yeyun Gong, et al. · 2023 - [Deductive Verification of Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/) — Zhan Ling, Yunhao Fang, Xuanlin Li, et al. · 2023 - [Distilling Reasoning Capabilities into Smaller Language Models](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/) — Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan · 2023 - [Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/) — Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al. · 2023 - [Faith and Fate: Limits of Transformers on Compositionality](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/) — Nouha Dziri, Ximing Lu, Melanie Sclar, et al. · 2023 - [Faithful Chain-of-Thought Reasoning](https://md.research.elamur.ai/free/en/papers/lyu-2023-faithful-chain-thought-reasoning/) — Qing Lyu, Shreya Havaldar, Adam Stein, et al. · 2023 - [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://md.research.elamur.ai/free/en/papers/rein-2023-gpqa-graduate-level-google-proof/) — David Rein, Betty Li Hou, Asa Cooper Stickland, et al. · 2023 - [Large Language Models are Better Reasoners with Self-Verification](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/) — Yixuan Weng, Minjun Zhu, Fei Xia, et al. · 2023 - [Large Language Models Are Reasoning Teachers](https://md.research.elamur.ai/free/en/papers/ho-2023-large-language-models-are-reasoning/) — Namgyu Ho, Laura Schmid, Se-Young Yun · 2023 - [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/) — Freda Shi, Xinyun Chen, Kanishka Misra, et al. · 2023 - [Large Language Models Cannot Self-Correct Reasoning Yet](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-cannot-self/) — Jie Huang, Xinyun Chen, Swaroop Mishra, et al. · 2023 - [LeanDojo: Theorem Proving with Retrieval-Augmented Language Models](https://md.research.elamur.ai/free/en/papers/yang-2023-leandojo-theorem-proving-retrieval-augmented/) — Kaiyu Yang, Aidan M. Swope, Alex Gu, et al. · 2023 [← prev](https://md.research.elamur.ai/free/en/shelves/mathematics/page/2/) [1](https://md.research.elamur.ai/free/en/shelves/mathematics/) [2](https://md.research.elamur.ai/free/en/shelves/mathematics/page/2/) **3** [4](https://md.research.elamur.ai/free/en/shelves/mathematics/page/4/) … [7](https://md.research.elamur.ai/free/en/shelves/mathematics/page/7/) [next →](https://md.research.elamur.ai/free/en/shelves/mathematics/page/4/)