Thanks for the feedback!

We chose a set of tasks with different levels of complexity to see how this approach would scale. For LLMs, the "challenge" with NER is not the task itself but the arbitrariness of the labels in the dataset. I agree it's still much simpler than the other tasks we present (agentic RAG, agentic tool use, maze navigation).

There are definitely strong parallels to model distillation and student-teacher training, with the primary difference being that we don't simply take all the data from the larger model but rather filter the dataset based on metrics from the environment. In the "Does curation even matter?" section, we show that this generally improves the result by a good margin.

We link to Vicuna, which might be the closest reference as prior art: https://lmsys.org/blog/2023-03-30-vicuna/

Thanks!

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!

With supervised fine-tuning (SFT), you'll often see good results with 100-1000+ datapoints (they can be variations of the same prompt template). If you have more limited data, reinforcement fine-tuning (RFT) can work well in the 10-100 range.

Good luck!

We set up dataset splits and the usual best practices. Of course, if you overdo things, you can still hack benchmarks; our goal isn't to publish SOTA numbers but rather to illustrate results from our methodology. We didn't even tune hyperparameters, we just used the default choices. Definitely a valid concern for teams chasing SOTA though.

Thanks!