3 ms·
For me this felt key to understanding the value vs tool calls: > Because the execution trace is part of the forward pass, the whole process remains differentia
by que3216 8mo ago
For me this felt key to understanding the value vs tool calls:
> Because the execution trace is part of the forward pass, the whole process remains differentiable: we can even propagate gradients through the computation itself. That makes this fundamentally different from an external tool. It becomes a trainable computational substrate that can be integrated directly into a larger model.
- D-Machine 8mo agoExcept their process isn't actually differentiable, as they admit near the end of the post, they just sort hand-wavily suggest that approximately differentiable methods "should" work. Also no mention at all of what the training data would be, where it would come from, or how a loss function could be constructed to continuously score "partially correct" programs (of what that would even mean, or if that idea is even coherent). What was a good point, mentioned by @hedgehog in this thread (https://news.ycombinator.com/item?id=47367986 https://news.ycombinator.com/item?id=47367986), is that tool-calls break batching a lot, so there could be huge efficiency gains at scale if you can just pass through a computation sub-network (even if that sub-net is frozen and can't be updated, and is programmed in manually rather than trained in). Why on Earth you'd want that sub-net to be a clunky transformer rather than just an efficient, GPU-accelerated custom non-trainable layer, though, is unclear to me.