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.
Мир Российская Премьер-лига|20-й тур
。wps是该领域的重要参考
FirstFT: the day's biggest stories
В школьном туалете нашли трехметрового питона14:50
。关于这个话题，谷歌提供了深入分析
В подписи к посту она поздравила женщин с 8 Марта. «Этот праздник — напоминание о справедливости и добре. Кто бы что ни говорил, женщиной быть прекрасно!» — заявила знаменитость.
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