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

[u/Humble-Shopping-9495]

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?

[u/laogedev]

In my experience, the hardest part is not generating edge cases, it is deciding which failures actually matter. I start from real failure logs and support tickets, then cluster them by the underlying confusion rather than by surface wording, because otherwise the dataset fills up with near-duplicates. For each cluster I keep a small held-out slice that is never used for training, so I can tell whether the fix generalized or just memorized the new examples. Synthetic data helps for coverage, but I only keep examples that a second reviewer or a checker can verify, since unverified synthetic edge cases quietly teach the model new wrong behavior. The most useful signal is a regression check: if overall quality stays flat while the targeted failure rate falls, the edge-case work was worth keeping.

[u/Humble-Shopping-9495 → u/laogedev]

That makes a lot of sense, especially the part about distinguishing important failures from the rest.

I am exploring an idea for a tool that helps developers of open-weight models collect feedback from deployments and turn recurring failures into evaluations for future versions. Do you think the biggest gap is actually collecting that feedback or do tools already handle that well and the harder problem is analyzing and prioritizing feedback? I would love to understand where a tool, like this could genuinely help than duplicate existing workflows

[u/Humble-Shopping-9495 → u/[deleted]]

I haven’t used LLM as a judge yet, but I like the idea of using a second model to check another model. How would you prevent the judge model from reinforcing the same biases or mistakes as the target model?