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NeurIPS 2025 | Language Ranker:从推荐系统的视角反思并优化大模型解码过程
4.Ranker Scaling Law:采样越多,性能越强 图表 8 Ranker Scaling Law 随着候选响应数量从 1 增加至 100,Language Ranker 在三项任务上均持续提升: 在 MATH 任务中,准确率从 25% 稳步上升至 56%; 在 MBPP 中,从 42% 上升至 59%; 在 xLAM 函数调用中,从 11% 提升至 47%。
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