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> should not discount that DeepSeek also gets paid in data, which is probably more valuable to them That's agentic feedback loops for training, right? Any more

> should not discount that DeepSeek also gets paid in data, which is probably more valuable to them That's agentic feedback loops for training, right? Any more detail on this, such as how they actually tell whether that data is good or not? That seems like a very hard problem, and like the value of that data is low compared to just building their own, controlled RL gyms.

gpugreg 2mo ago
Agents usually start with ingesting the existing code base, and DeepSeek can use those code bases for pretraining. And they will have filters on top of that to throw out garbage. I am not sure how they are using the data for post-training, but there probably are ways to get signal out of it, e.g. sentiment analysis when the user begins cursing at the agent, or checking whether the user continued another session with the generated code, or started a new session with the same starting point as before, i.e. they git-stashed. Generally, you can train on data that is quite bad (e.g. the entire internet). It will still work, but take much longer compared to clean data.
- throw10920 2mo ago
  > e.g. sentiment analysis when the user begins cursing at the agent, or checking whether the user continued another session with the generated code, or started a new session with the same starting point as before, i.e. they git-stashed. Thank you for elaborating, that's already useful. Anywhere I can learn more about this? I'm very interested in it!
  - gpugreg 2mo ago
    To learn about sentiment analysis, I'd look for related datasets and then look at recent code, e.g. here: https://www.kaggle.com/datasets?search=sentiment+analysis https://www.kaggle.com/datasets?search=sentiment+analysis For more LLM-specific stuff, you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces https://huggingface.co/datasets?format=format%3Aagent-traces and check out what people are doing with it (usually linked on the right when you click on a dataset). And of course https://scholar.google.com/ https://scholar.google.com/ for research papers.

gpugreg 2mo ago

Agents usually start with ingesting the existing code base, and DeepSeek can use those code bases for pretraining. And they will have filters on top of that to throw out garbage. I am not sure how they are using the data for post-training, but there probably are ways to get signal out of it, e.g. sentiment analysis when the user begins cursing at the agent, or checking whether the user continued another session with the generated code, or started a new session with the same starting point as before, i.e. they git-stashed. Generally, you can train on data that is quite bad (e.g. the entire internet). It will still work, but take much longer compared to clean data.

throw10920 2mo ago
> e.g. sentiment analysis when the user begins cursing at the agent, or checking whether the user continued another session with the generated code, or started a new session with the same starting point as before, i.e. they git-stashed. Thank you for elaborating, that's already useful. Anywhere I can learn more about this? I'm very interested in it!
- gpugreg 2mo ago
  To learn about sentiment analysis, I'd look for related datasets and then look at recent code, e.g. here: https://www.kaggle.com/datasets?search=sentiment+analysis https://www.kaggle.com/datasets?search=sentiment+analysis For more LLM-specific stuff, you can pick some agent trace dataset on https://huggingface.co/datasets?format=format%3Aagent-traces https://huggingface.co/datasets?format=format%3Aagent-traces and check out what people are doing with it (usually linked on the right when you click on a dataset). And of course https://scholar.google.com/ https://scholar.google.com/ for research papers.