SAPO:一种稳定且高性能的大语言模型强化学习方法
强化学习(Reinforcement Learning, RL)已经成为提升大语言模型(Large Language Models, LLM)推理能力的核心技术之一。现代 RL 训练流程使模型能够解决困难的数学问题、编写复杂代码和进行多模态推理。实践中,一种被广泛采用的方法是基于组的策略优化(group‑based policy optimization):对每个提示采样多个回复,并在组内进行奖励归一化。
外观
强化学习(Reinforcement Learning, RL)已经成为提升大语言模型(Large Language Models, LLM)推理能力的核心技术之一。现代 RL 训练流程使模型能够解决困难的数学问题、编写复杂代码和进行多模态推理。实践中,一种被广泛采用的方法是基于组的策略优化(group‑based policy optimization):对每个提示采样多个回复,并在组内进行奖励归一化。
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