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About preference-optimization
preference-optimization
Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO
preference-optimization is an agent skill from the wshobson/agents collection for aligning a fine-tuned language model with preference data, using methods from the DPO family: DPO, ORPO, KTO and SimPO. Preference data comes either as pairs, where a reviewer picks which of two responses is better, or as unpaired thumbs-up and thumbs-down feedback. The skill applies when that kind of feedback exists and the model should learn from it rather than from demonstrations or a verifiable reward. Within the wshobson fine-tuning sequence, the routing step is assumed to have already sent the work here, and the other skills cover demonstration-based training and reward-based reinforcement learning. The skill covers the choice of method within the DPO family, the evidence on how much that choice actually changes results, the production training pattern, and how to build the preference pairs in the first place. Preference data is only as good as the judgments behind it, so the skill's guidance on building pairs matters as much as the training configuration. The skill is installed with the skills CLI from the wshobson/agents repository. Its skills.sh page showed about 2.4K installs and passing results from three security audits at the time of the scan.
- Price
- Free
- Made by
- wshobson
- Category
- AI Skills
- Website
- github.com/wshobson/agents/preference-optimization