从理论视角分析VAPO框架的局限性
机器学习
2025-05-28 v2 计算与语言
摘要
VAPO框架在处理大型语言模型（LLM）中长链思维（CoT）推理任务时，已显示出显著的经验性成功，通过系统性地解决价值模型偏差、异构序列长度和稀疏奖励信号等挑战，实现了最先进的性能。尽管其实际效益显而易见，但对其底层机制及潜在局限性的更深入理论理解对于指导未来发展至关重要。本文旨在从理论角度启动此类讨论，探讨VAPO在其假设可能受到挑战的领域，以及进一步研究可能产生更稳健和更通用推理代理的区域。我们深入探讨了在复杂推理空间中进行价值函数近似的细节、自适应优势估计的优化性、标记级优化的影响，以及探索和泛化的持久挑战。
引用
@article{arxiv.2505.17997,
title = {Towards Analyzing and Understanding the Limitations of VAPO: A Theoretical Perspective},
author = {Jintian Shao and Yiming Cheng and Hongyi Huang and Beiwen Zhang and Zhiyu Wu and You Shan and Mingkai Zheng},
journal= {arXiv preprint arXiv:2505.17997},
year = {2025}
}
备注
We are withdrawing this submission as the underlying experiment is currently incomplete. We require additional time to gather more data and supplement the existing findings to ensure a comprehensive and robust presentation. We intend to resubmit once these additions are finalized