Current embeddings are badly trained and are massively holding back networks. A core issue is something I call 'token drag'. Low frequency tokens, when they finally come up, drag the model back towards an earlier state causing a lot of lost training. This leads to the first few layers of a model effectively being dedicated to just being a buffer to the bad embeddings feeding the model. Luckily fixing this is actually really easy. Creating a sacrificial two layer network to predict embeddings in training (and then just calculating the embeddings once for prod inference) gives a massive boost to training. To see this in action check out the unified embeddings in this project: https://github.com/jmward01/lmplay
Do you have a peer reviewed source you could link on this approach, or is it something you thought of and are experimenting with yourself? I couldn't tell from the LMPlay repo in my skim, and the idea is intriguing
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Current embeddings are badly trained and are massively holding back networks. A core issue is something I call 'token drag'. Low frequency tokens, when they finally come up, drag the model back towards an earlier state causing a lot of lost training. This leads to the first few layers of a model effectively being dedicated to just being a buffer to the bad embeddings feeding the model. Luckily fixing this is actually really easy. Creating a sacrificial two layer network to predict embeddings in training (and then just calculating the embeddings once for prod inference) gives a massive boost to training. To see this in action check out the unified embeddings in this project: https://github.com/jmward01/lmplay
Do you have a peer reviewed source you could link on this approach, or is it something you thought of and are experimenting with yourself? I couldn't tell from the LMPlay repo in my skim, and the idea is intriguing
all my own ideas in there. I was thinking of writing it up more formally, but I am more of a 'think -> build -> next thing' kind of person.
Oh wow, great set of reads. Thanks to @sean_pedersen for posting, looking forward to reviewing this in my closeout this year.