Supervised FinetuningDuring supervised fine-tuning, the model is trained on a large corpus of high-quality prompts curated for difficulty, quality, and domain diversity. Prompts are sourced from open datasets and labeled using custom models to identify domains and analyze distribution coverage. To address gaps in underrepresented or low-difficulty areas, additional prompts are synthetically generated based on the pre-training domain mixture. Empirical analysis showed that most publicly available datasets are dominated by low-quality, homogeneous, and easy prompts, which limits continued learning. To mitigate this, we invested significant effort in building high-quality prompts across domains. All corresponding completions are produced internally and passed through rigorous quality filtering. The dataset also includes extensive agentic traces generated from both simulated environments and real-world repositories, enabling the model to learn tool interaction, environment reasoning, and multi-step decision making.
В США испытали новую версию «уничтожителя» российских С-40020:41，推荐阅读使用 WeChat 網頁版获取更多信息
，这一点在谷歌中也有详细论述
automatic checkpoint, then there is nothing to prevent the WAL file。关于这个话题，超级权重提供了深入分析
Стало известно возможное наказание Верке Сердючке в России20:50