It depends on what that ’40% staff cost’ means, really. Was it just accounting shenanigans related to RSUs and GOOG stock fluctuations? Then it means pretty much nothing of interest to us here at LW. Did it come from shedding a few superstars with multi-million-dollar compensation packages? Hard to say, depends on how much you think superstars matter at this point compared to researchers. Could be a very big deal: I remain convinced that search for LLMs may be the Next Big Thing and everyone who is reinventing RL from scratch for LLMs is botching the job, and so a few superstar researchers leaving DM could be critical. (But maybe you think the opposite because it’s now all about big pressgangs of researchers whipping a model into shape.) Did it come from shedding a lot of lower-level people who are obscure and unheard of? Inverse of the former.
If the cut is inflated by Edmonton people getting the axe, then I personally would consider this cut to be irrelevant: I have been largely unimpressed by their work, and I think Sutton’s ‘Edmonton plan’ or whatever he was calling it is not an interesting line of work compared to more mainstream RL scaling approaches. (In general, I think Sutton has completely missed the boat on deep learning & especially DL scaling. I realize the irony of saying this about the author of “The Bitter Lesson”, but if you look at his actual work, he’s committed to basically antiquated model-free tweaks and small models, rather than the future of large-scale model-based DRL—like all of his stuff on continual learning is a waste of time, when scaling just plain solves that!)
It depends on what that ’40% staff cost’ means, really. Was it just accounting shenanigans related to RSUs and GOOG stock fluctuations? Then it means pretty much nothing of interest to us here at LW. Did it come from shedding a few superstars with multi-million-dollar compensation packages? Hard to say, depends on how much you think superstars matter at this point compared to researchers. Could be a very big deal: I remain convinced that search for LLMs may be the Next Big Thing and everyone who is reinventing RL from scratch for LLMs is botching the job, and so a few superstar researchers leaving DM could be critical. (But maybe you think the opposite because it’s now all about big pressgangs of researchers whipping a model into shape.) Did it come from shedding a lot of lower-level people who are obscure and unheard of? Inverse of the former.
If the cut is inflated by Edmonton people getting the axe, then I personally would consider this cut to be irrelevant: I have been largely unimpressed by their work, and I think Sutton’s ‘Edmonton plan’ or whatever he was calling it is not an interesting line of work compared to more mainstream RL scaling approaches. (In general, I think Sutton has completely missed the boat on deep learning & especially DL scaling. I realize the irony of saying this about the author of “The Bitter Lesson”, but if you look at his actual work, he’s committed to basically antiquated model-free tweaks and small models, rather than the future of large-scale model-based DRL—like all of his stuff on continual learning is a waste of time, when scaling just plain solves that!)