Why use L2 regularization in reward model training?

LAION-AI / Open-Assistant

OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.

https://open-assistant.io

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Why use L2 regularization in reward model training? #3675

Open hannlp opened 1 year ago

hannlp commented 1 year ago

Hello, respected developers of Open Assistant. @andreaskoepf While studying your reward model training code, I noticed that besides the ranking loss, there is an additional L2 regularization term. What is the purpose of this regularization term? Are there any papers that mention it? https://github.com/LAION-AI/Open-Assistant/blob/7e40ee313bd327ca069e1d8b38efc371b66dea6f/model/model_training/utils/losses.py#L76

Ravenclaw1 commented 1 year ago

My apologies I don’t know if there is any research that covers it as I am new to the AI system. However I can explain that I am interested in attempting to make a conversational AI companion that responds with miare personality so it’s less boring, I understand if you would like me to cease some of my testing on your online version.-- The one, the only, Kaegan R. Bruce