15+ Premium newsletters by leading experts
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.
。关于这个话题，新收录的资料提供了深入分析
移动游戏收入63.9亿、同比增长14%；
This isn't a theoretical concern. Kagan Yilmaz documented it well in his analysis of CLI vs MCP costs, showing that 6 MCP servers with 84 tools consume ~15,540 tokens at session start. His project CLIHub demonstrated that converting MCP servers to CLIs and letting the LLM discover tools on-demand slashes that cost by 92-98%.