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64 lines
2.8 KiB
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64 lines
2.8 KiB
TeX
\section*{Acknowledgments}
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This work was supported by Zoo Labs Foundation Inc, a 501(c)(3) non-profit organization dedicated to AI democratization. We thank the Tencent youtu-agent team for pioneering training-free GRPO, the Hugging Face community for open-source infrastructure, and the 1,247 KEEPER token holders who participated in early governance experiments.
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\bibliographystyle{plain}
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\begin{thebibliography}{99}
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T. Brown et al., ``Language models are few-shot learners,'' \textit{NeurIPS}, 2020.
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P. Christiano et al., ``Deep reinforcement learning from human preferences,'' \textit{NeurIPS}, 2017.
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\bibitem{harris2019decentralized}
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J. Harris and B. Waggoner, ``Decentralized and collaborative AI on blockchain,'' \textit{IEEE BigData}, 2019.
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\bibitem{johnson2019billion}
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J. Johnson et al., ``Billion-scale similarity search with GPUs,'' \textit{IEEE Trans. Big Data}, 2019.
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\bibitem{kairouz2021advances}
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P. Kairouz et al., ``Advances and open problems in federated learning,'' \textit{Found. Trends ML}, 2021.
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\bibitem{lewis2020retrieval}
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P. Lewis et al., ``Retrieval-augmented generation for knowledge-intensive NLP tasks,'' \textit{NeurIPS}, 2020.
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\bibitem{mcmahan2017communication}
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B. McMahan et al., ``Communication-efficient learning of deep networks from decentralized data,'' \textit{AISTATS}, 2017.
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\bibitem{ouyang2022training}
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L. Ouyang et al., ``Training language models to follow instructions with human feedback,'' \textit{NeurIPS}, 2022.
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\bibitem{rafailov2023direct}
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R. Rafailov et al., ``Direct preference optimization: Your language model is secretly a reward model,'' \textit{NeurIPS}, 2023.
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\bibitem{schulman2017proximal}
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J. Schulman et al., ``Proximal policy optimization algorithms,'' \textit{arXiv:1707.06347}, 2017.
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\bibitem{shao2024deepseekmath}
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Z. Shao et al., ``DeepSeekMath: Pushing the limits of mathematical reasoning in open language models,'' \textit{arXiv:2402.03300}, 2024.
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\bibitem{tencentyoutu2025grpo}
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Tencent youtu-agent team, ``Training-free group relative policy optimization,'' \textit{arXiv:2510.08191}, 2025.
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\bibitem{wang2022self}
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X. Wang et al., ``Self-consistency improves chain of thought reasoning in language models,'' \textit{ICLR}, 2022.
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\bibitem{wei2022chain}
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J. Wei et al., ``Chain-of-thought prompting elicits reasoning in large language models,'' \textit{NeurIPS}, 2022.
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\bibitem{yao2023tree}
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S. Yao et al., ``Tree of thoughts: Deliberate problem solving with large language models,'' \textit{NeurIPS}, 2023.
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\end{thebibliography}
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\appendix
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