LoopQ: Quantization for Recursive Transformers

In three linesLoopQ introduces a post-training quantization (PTQ) method for recursive language models (LoopLMs) that reuse Transformer blocks. It addresses three challenges: distribution shift across roles, state reuse between loops, and recursive error accumulation. Results: +68.8% downstream accuracy and -87.7% perplexity reduction in W4A4 vs strongest static PTQ baseline.

Summary generated by Claude — human-verified