Do Halting Heads Really Save Compute in Looped Language Models?
Compares the stopping probabilities predicted by halting heads with the iterations actually executed. Useful early exits require training every iteration to predict tokens and a strong penalty favoring early stopping; batches that wait for their slowest token erase most of the advantage.
First public: · Added to this list: October 26, 2026
Explore this approach
Recurrent reasoning — Repeat parts of the model or keep intermediate information in memory while working through a problem. Some methods also learn how many passes to run.
Analysis and evaluation — Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.