LAION-AI / LAION-AI/Open-Assistant
Why use L2 regularization in reward model training?
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Description
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
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Research direction
Start with model/model_training/utils/losses.py at the linked line and inspect how the reward model ranking loss and L2 term are used. Research the purpose of this regularization in reward model training and identify relevant papers. Done means documenting a clear explanation with supporting citations.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100