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Increasing test-time compute for LLMs shows promise across domains but remains underexplored in code generation, despite extensive study in math.
On the measure of intelligence
François Chollet. 2019 · 1911
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Deep reinforcement learning from human preferences
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Evaluating large language models trained on code
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Competition-level code generation with alphacode
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Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022 · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
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Rewarding chatbots for real-world engagement with millions of users
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Swe-bench: Can language models resolve real-world github issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. 2023 · 2023
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Dspy: Compiling declarative language model calls into self-improving pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T Joshi, Hanna Moazam, et al. 2023 · 2023
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Taco: Topics in algorithmic code generation dataset
Rongao Li, Jie Fu, Bo-Wen Zhang, Tao Huang, Zhihong Sun, Chen Lyu, Guang Liu, Zhi Jin, and Ge Li. 2023 · 2023
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Jiawei Liu, Chunqiu Steven Xia, Yuyao Wang, and Lingming Zhang. 2023 · 2023
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Large language monkeys: Scaling inference compute with repeated sampling
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Qwq: Reflect deeply on the boundaries of the unknown
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Code generation with alphacodium: From prompt engineering to flow engineering
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Archon: An architecture search framework for inference-time techniques
Jon Saad-Falcon, Adrian Gamarra Lafuente, Shlok Natarajan, Nahum Maru, Hristo Todorov, Etash Guha, E Kelly Buchanan, Mayee Chen, Neel Guha, Christopher Ré, et al. 2024 · 2024
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Scaling llm test-time compute optimally can be more effective than scaling model parameters
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Llms can easily learn to reason from demonstrations structure, not content, is what matters!
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