A 432-parameter RNN still defies full interpretability, ex-OpenAI researcher shows
JacobHHilton · x · 2026-10-16
- Former OpenAI researcher Jacob Hilton released a recurrent neural network with only 432 parameters — small enough to fit its weights in a tweet — trained to find the second largest number in a sequence.
- Even at this scale, current interpretability techniques cannot achieve complete understanding of the model; he published a video showing everything currently known about its internals, plus downloadable weights.
- The demo makes a pointed argument: the bottleneck of mechanistic understanding lies in the methods themselves, not model size.
Related event: 432-parameter model still unexplainable, sparking interpretability debate(3 posts)→
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