So
pre-training → CPT/mid-training → SFT → {RL, many experts} → MOPD warm-up / SFT → MOPD → maybe loop a few times
This is starting to look like the pipelines we had before deep learning.
Markus Wulfmeier compares this complexity to Kuhn's paradigm shifts.
So
pre-training → CPT/mid-training → SFT → {RL, many experts} → MOPD warm-up / SFT → MOPD → maybe loop a few times
This is starting to look like the pipelines we had before deep learning.
Users affirm the growing complexity of AI training pipelines with multi-stage post-training loops, recognizing parallels to elaborate older NLP setups that still underfit.
@DhruvBatra_ we deleted feature engineering only to invent training engineering
So
pre-training → CPT/mid-training → SFT → {RL, many experts} → MOPD warm-up / SFT → MOPD → maybe loop a few times
This is starting to look like the pipelines we had before deep learning.
Not an argument for it but the current cycle between simplicity and complexity feels quite natural. When squinting, this is Kuhn's argument around progress in science and Gabriel's for software.
New paradigm enables and even forces simplicity, because it works well in its pure form and because we don't understand it well enough yet to improve it with complex variants.
Over time improvements become more specific (to enable short term gains) and harder to build upon due to complexity correlations across all previous variants.
We get fed up with complexity, improvements are slower, and after a while we find a new paradigm...
So
pre-training → CPT/mid-training → SFT → {RL, many experts} → MOPD warm-up / SFT → MOPD → maybe loop a few times
This is starting to look like the pipelines we had before deep learning.

@0x00_void Amen!

@DhruvBatra_ real recognize real. those old NLP pipelines had more stages than a Marvel movie and still underfit.

@DhruvBatra_ It's also a very human thing in general. I wonder if there's work relating it to organizations and political systems..