技能创建器
skill-creator
创建、改进和评估自定义技能,把专业流程转化为可重复使用的 AI 能力。
preference-training【数据与研究】Plan Qwen-VL DPO and GRPO training commands, reward functions, and reasoning-aware preference data.
独立 GitHub 快照 · 自动分类 · 以原作者文档为准
Use this sub-skill for DPO, GRPO, reward-function planning, and reasoning-aware preference data.
../../references/workflow-map.md for route confirmation.../../references/data-formats.md for DPO and reasoning schemas.../../references/model-compatibility.md for model-family and backend caveats.../../references/cli-reference.md for the shared flag surface.references/rewards.md for reward discovery and built-in reward examples.references/troubleshooting.md for preference-training failures.scripts/preference_command.py for a safe command builder.chosen and rejected samples._reward.Qwen3.5 may mix reasoning and non-reasoning samples.Qwen3-VL-*-Thinking requires reasoning on every assistant turn when reasoning is enabled.dr_grpo, remind them that the repo docs require --max_completion_length.Start with the bundled helper:
python scripts/preference_command.py --help
python scripts/preference_command.py --mode dpo --model-id Qwen/Qwen2.5-VL-3B-Instruct --data-path data/dpo.json --image-folder data/images --output-dir outputs/dpo
Read references/rewards.md for reward-function conventions and references/troubleshooting.md for backend and schema issues.