Is it legitimate to train a reranker on GPT-4-generated...

A strong answer avoids a simple yes/no and instead probes the relationship between the generator and the target model. You must determine if the generator is downstream of the model being trained and if the prompt generator covers the actual traffic served. The decision to use synthetic data should be based on the coverage floor, as synthetic data can outperform human-labeled sets if the coverage is sufficient. A weak answer relies on dogmatic policies, such as ‘synthetic is always fine’ or ‘only human labels are valid,’ without providing an analytical framework for when these approaches succeed or fail.