Can Small Models Overthink? TaH Skips 93% Redundant Iterations and Boosts Accuracy

TaH, a selective latent‑iteration method for small language models, identifies and avoids unnecessary token‑level loops, cutting about 93% of extra iterations while delivering a stable 3.0%‑6.8% accuracy gain across nine math, QA, and code benchmarks.

LLM reasoningLooped TransformerTaH

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Can Small Models Overthink? TaH Skips 93% Redundant Iterations and Boosts Accuracy