I couldn't find a concrete answer anywhere, do you just distill the instruct model back? If that is the case, what is a quality european language question set to turn it back into a chatbot/agentic, can a model at that size even be agentic? (i chose this size to learn) if i finetune for my specific harness? (i have a lot of training data of opus running in my harness) my harness basically has the model output python code and has a few built-in functions like: - vector_search_laws() - graph_search() could i have the model at least internalize a "hunch" on what stuff to search? also what is the latest RL technique for agentic/harnes specific workflows? I have a lot of RAW training data, like court decisions or commentaries or legislature, but not a lot of golds. could i use these to synthesize training data and maybe RL the model in my harness to find that data? What would y'all's strategy in the CPT->SFT->RL pipeline be for my specific problem? I know this is a lot of questions im trying to figure out which direction to go, any pointers? Also good resources are welcome, for example that alex karpathi video was amazing for me, but i'd imagine its a bit outdated in terms of latest RL and SFT?   submitted by   /u/SignificantZebra5883 [link]   [comments]