What's the hardest part of creating edge cases for fine-tuning open-weight models? [D]

I'm interested in how people build datasets for fine-tuning open-weight models, especially when it is about making a model handling edge cases than just perform well on common examples. I'm curious about the data side of the process. Finding edge cases: How do you identify edge cases that are rare or unusual. Also how do you make edge cases on an existing dataset to make it more specialized for what you need. Generating examples: Do you write them by yourself, or use a good model to generate synthetic data, mine real data, etc? Quality control: How do you catch incorrect, repetitive, or unrealistic examples? Coverage: How do you know when your dataset covers enough of the failure cases that matter? Measuring impact: Have you had cases where adding more edge cases actually made the model worse or failed to improve it? submitted by /u/Humble-Shopping-9495 [link] [comments]

Related

• augmenting large datasets to have more edge case data for training [D]
• What should happen when you feed impossible moves into a chess-playing language model? [D]
• How are you handling training data when public datasets don't match your use case? [D]

Source: r/MachineLearning | 2026-10-25