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Looped Transformers can achieve tens of thousands of effective reasoning steps without performance collapse by learning to selectively retain and decay intermediate states.
Deterministic decoding can outperform stochastic self-consistency in constrained domains by systematically exploring high-probability reasoning traces, leading to better performance with less computation.
