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Is this just distillation but with a step to filter out low-quality responses first?

AFAIK, distillation typically refers to tuning on the logits of the larger model, so you wouldn't be able to do that with fine-tuning APIs (OpenAI + Google in our blog post). We fine-tune on the outputs themselves.

But broadly speaking, yes, we generate data using a large model, curate the best samples using metrics from the environment, and fine-tune on that data. This isn't a novel technique from an academic perspective; our focus is on applying it to different use cases (e.g. agentic RAG, agentic tool use) and models (OpenAI, Google, Qwen).

Thanks!

Thanks for the explanation and the clarification on terminology! I've used a similar approach myself and it sounded like you were doing something similar.

> AFAIK, distillation typically refers to tuning on the logits of the larger model

I think this is called “logit distillation” which is a particular form of distillation but not the only one.

> so you wouldn't be able to do that with fine-tuning APIs (OpenAI + Google in our blog post)

Dististillation from competitors' API is so common it has been given a name: it's called “distealing”.

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