LMCA that uses a body of knowledge in the form of textbooks, scientific theories and models may be updated very frequently and cheaply: essentially, every update of the scientific textbook is an update of LMCA. No need to re-train anything.
GFlowNets have a disadvantage because they are trained for a very particular version of the exemplary actor, drawing upon a particular version of the body of knowledge. And this training will be extremely costly (billions or tens or even hundreds of billions of USD?) and high-latency (months?). By the time a hypothetical training of GFlowNet is complete, the textbooks and models may be already outdated.
This consideration really challenges the economic and capability expediency of GFlowNet actors (as described in the post) vs. LMCA. The flexibility, deployability, configurability, and iterability of LMCA may prove to be a too strong factor.
LMCA that uses a body of knowledge in the form of textbooks, scientific theories and models may be updated very frequently and cheaply: essentially, every update of the scientific textbook is an update of LMCA. No need to re-train anything.
GFlowNets have a disadvantage because they are trained for a very particular version of the exemplary actor, drawing upon a particular version of the body of knowledge. And this training will be extremely costly (billions or tens or even hundreds of billions of USD?) and high-latency (months?). By the time a hypothetical training of GFlowNet is complete, the textbooks and models may be already outdated.
This consideration really challenges the economic and capability expediency of GFlowNet actors (as described in the post) vs. LMCA. The flexibility, deployability, configurability, and iterability of LMCA may prove to be a too strong factor.