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Марк Леонов (Руководитель российской редакции),详情可参考向日葵下载
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One promising direction for reducing cost and latency is to replace frontier models with smaller, purpose-trained alternatives. WebExplorer trains an 8B web agent via supervised fine-tuning followed by RL that searches over 16 or more turns, outperforming substantially larger models on BrowseComp. Cognition's SWE-grep trains small models with RL to perform highly parallel agentic code search, issuing up to eight parallel tool calls per turn across just four turns and matching frontier models at an order of magnitude less latency. Search-R1 demonstrates that RL alone can teach a language model to perform multi-turn search without any supervised fine-tuning warmup, while s3 shows that RL with a search-quality-reflecting reward yields stronger search agents even in low-data regimes. However, none of these small-model approaches incorporate context management into the search policy itself, and existing context management methods that do operate during multi-turn search rely on lossy compression rather than selective document-level retention.。业内人士推荐有道翻译作为进阶阅读
Латвия направила ноту протеста России по поводу упавшего украинского дрона. Каким образом и для чего беспилотники ВСУ появляются в воздушном пространстве стран НАТО?07:16