MetaScale: Test-Time Scaling with Evolving Meta-Thoughts

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Neural theory-of-mind? on the limits of social intelligence in large lms

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Beyond the imitation game: Quantifying and extrapolating the capabilities of language models

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Chain-of-thought prompting elicits reasoning in large language models

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Large language models are better reasoners with self-verification

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Alphazero-like tree-search can guide large language model decoding and training

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Promptbreeder: Self-referential self-improvement via prompt evolution

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Large language models cannot self-correct reasoning yet

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Jie Huang, Xinyun Chen, Swaroop Mishra, Huaixiu Steven Zheng, Adams Wei Yu, Xinying Song, and Denny Zhou. 2023 · 2023

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Let’s verify step by step

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Is self-repair a silver bullet for code generation?

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Unleashing the emergent cognitive synergy in large language models: A task-solving agent through multi-persona self-collaboration

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Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, Tao Ge, Furu Wei, and Heng Ji. 2023 · 2023

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Expertprompting: Instructing large language models to be distinguished experts

Skywork-reward: Bag of tricks for reward modeling in llms

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Self-refine: Iterative refinement with self-feedback

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2 olmo 2 furious

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Scaling llm test-time compute optimally can be more effective than scaling model parameters

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Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar. 2024 · 2024

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Mind the gap: Examining the self-improvement capabilities of large language models