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@@ -10,7 +10,7 @@ Data can generally be divided along two axis:
- high quality 🗹 or Lower quality ☐
- natural 🧑 or unnatural 🤖
Depending on your training objectives you will want lots of low quality instruction data, or a small amount of high quality data. Which should you use? Lets see what Anthropic have to say [Askell et all Antrhopic](https://arxiv.org/abs/2112.00861)]:
Depending on your training objectives you will want lots of low quality instruction data, or a small amount of high quality data. Which should you use? Lets see what Anthropic have to say in [Askell et al](https://arxiv.org/abs/2112.00861):
> **How can we improve the sample efficiency of preference modeling?** We find that we can significantly improve sample efficiency using a ‘preference model pre-training’ (PMP) stage of training, where we first pre-train on large public datasets that encode human preference information, such as Stack Exchange, Reddit, and Wikipedia edits, before finetuning on smaller datasets encoding more specific human preferences.