DeepSeek 与清华联合研究:创新奖励模型推理方法，提升可扩展性
近日，DeepSeek 和清华的研究者发布新论文，探讨了奖励模型的推理时 Scaling 方法，让 DeepSeek R2似乎更近一步。目前，强化学习在大语言模型的大规模后训练阶段广泛应用，但面临为大语言模型获取准确奖励信号的挑战。研究者发现，采用点式生成式奖励建模（GRM）能提升模型适应能力和推理阶段可扩展性。为此，他们提出自我原则点评调优(SPCT)学习方法，经此训练得到 DeepSeek - GRM 模型，如基于 Gemma -2-27B 训练的 DeepSeek - GRM -27B。实验显示，SPCT 显著提高了 GRM 的质量和可扩展性，在多个基准
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