He stated in interviews "Wir werden das blöde GPT einfach wegkicken" (roughly: We will simply kick silly GPT off the pitch) and he just founded a company to secure funding. Interesting times.
Someone gathered most of the available information here: https://github.com/AI-Guru/xlstm-resources
Being a researcher at a public university in a country that doesn't exactly splurge on this kind of research he has to get creative to get any meaningful amount of funding.
So I guess what's left is doing these grand proclamations that you are going to "knock the crown off OpenAI" etc. Though, some sort of vision is good to have for sure :)
The benchmarking done in the table 1 is extremely questionable. Their table basically contradicts the results from multiple peer reviewed papers, especially for RNNs which report results much closer to baseline transformers (and conducted much larger experiments btw).
Page 40 they mention that all models are trained with the same lr for comparability.
> Contradicts their own scaling laws table which uses different lr for different models
> And no it is not a fair comparison to use the same lr to test all these different models. Benchmarking results just looks like they are using tuned hyperparameters for their model which happens to not work for other models.
RWKV-v6 > RWKV-v5 > RWKV-v4, not the other way round obviously. HGRN 8 ppl worse than baseline transformers? NIPS 2023 spotlight paper btw.
However they completely messed up benchmarking experiments for various RNN models which in their papers claim comparable and even better performance than base transformer.