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arXiv · 2605.16343

LoopQ: Quantization for Recursive Transformers

Rui Fang·Hsi-Wen Chen·Ming-Syan Chen
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Abstract

Looped language models (LoopLMs) improve parameter efficiency by recursively reusing Transformer blocks, enabling deeper computation under a fixed model size. However, this reuse makes LoopLMs more fragile under post-training quantization (PTQ). We present the first systematic study of quantization in LoopLMs and identify three challenges: distribution shift across roles, state reuse across loop transitions, and recursive error accumulation. To address these challenges, we propose LoopQ, a loop-aware PTQ framework that preserves a shared quantized backbone while introducing lightweight adaptations. LoopQ combines activation scaling, selective transformation, cross-loop state alignment, and trajectory-aware optimization to reduce distributional mismatch within loops and error accumulation across loops. Experiments across seven benchmarks show that, under W4A4 quantization, LoopQ improves average downstream accuracy by 68.8% and reduces average perplexity by 87.7% compared with the strongest static PTQ baseline.

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Rui Fang, Hsi-Wen Chen, Ming-Syan Chen. 2026-05-08. LoopQ: Quantization for Recursive Transformers. https://arxiv.org/abs/2605.16343

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