Tencent：DRPO LLM强化学习的散度正则化策略解决大模型推理崩塌

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这项研究介绍了一种名为 DRPO 的新型大语言模型强化学习优化方法。针对现有方法如 PPO 和 SPO 在处理长尾词汇时因依赖重要性比率而导致的不稳定问题，DRPO 采用了一种基于绝对概率偏移的平滑二次正则化器。该方法不仅保留了 DPPO 在散度控制上的几何优势，还通过连续的梯度权重解决了硬掩码机制带来的信号中断问题。实验证明，DRPO 在多种模型规模、架构及精度设定下，均能显著提升训练的稳定性与效率。研究强调，在设计正则化项时，诱导出的梯度形式相比单纯的散度指标对优化效果更为关键。

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Tencent：DRPO LLM强化学习的散度正则化策略解决大模型推理崩塌

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