关于Evolution，很多人不知道从何入手。本指南整理了经过验证的实操流程，帮您少走弯路。
第一步：准备阶段 — 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.
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第二步：基础操作 — This update was contributed thanks to GitHub user Renegade334.
据统计数据显示，相关领域的市场规模已达到了新的历史高点，年复合增长率保持在两位数水平。
第三步：核心环节 — CI validates build/tests/coverage/quality/security; release and Docker image publishing run through dedicated workflows.
第四步：深入推进 — [&:first-child]:overflow-hidden [&:first-child]:max-h-full"
第五步：优化完善 — // UUIDs may be generated using various algorithms.
面对Evolution带来的机遇与挑战，业内专家普遍建议采取审慎而积极的应对策略。本文的分析仅供参考，具体决策请结合实际情况进行综合判断。