The unified framework replaces separate pretraining, SFT, and RLHF phases.
The field of AI is at a local minimum. Not a local minimum in architectures and models, but a local minimum on how we train: a Frankenstein multi-stage approach. In this new blog entry, I propose a different route based on continual interaction and causality.
https://love4all.ai/blog/continual-interactive-causal-agents/
Users like Nando de Freitas' proposal for single-stream interactive training of AI agents because they find his take on causality interesting and the overall direction promising.
@Michael_J_Black Thanks, Michael. It’s still not very developed or properly tested, but I agree that directionally, it feels worth trying
@NandoDF I like this. Directionally, it feels right.
@NandoDF I like this. Directionally, it feels right.
@NandoDF multi-stage pipeline feels like duct taping training phases together
the interventional agent is cleaner but do we have the infra to pull it off
@NandoDF the single stream feels inevitable tbh, but the gap between proposing it and making it work is where the real friction lives
@NandoDF this take on causality is super interesting, how did you start?
@NandoDF single stream agent approach would def reduce all the prompt engineering hell we deal with now
question is what happens to fine tuning in that setup
@NandoDF hard to disagree that multi-stage feels like patching a leak with more patches
wonder if the compute budget holds up in practice though
@NandoDF curious how u see continual interaction scaling in practice tho
most labs cant even keep one training run stable
The unified framework replaces separate pretraining, SFT, and RLHF phases.
The field of AI is at a local minimum. Not a local minimum in architectures and models, but a local minimum on how we train: a Frankenstein multi-stage approach. In this new blog entry, I propose a different route based on continual interaction and causality.
https://love4all.ai/blog/continual-interactive-causal-agents/