Quantizing Recursive Reasoning Models

Recursive reasoning models solve hard puzzles by applying compact, weight-tied blocks over many refinement steps. Because these blocks are reused many times, quantizing them creates a severe error accumulation challenge: the quantization error is incurred at every step. While 8-bit quantization (integer or float) preserves accuracy, moving to a per-tensor 4-bit format causes a systematic bias to accumulate. The ensuing drift catastrophically degrades exact-solution accuracy on Sudoku from 84.1% to 0.0% (only  25% of cells correct). In this work, we show that this collapse is caused by activation-scaling granularity rather than bit-width or number format. Crucially, moving to per-block scaling restores stable contraction and most of the lost accuracy. To implement this, we apply MXINT4, a blockwise integer format for weights and activations, to recursive reasoning models. It is competitive with blockwise float formats on our tasks, while keeping integer elements and power-of-two block scales. Finally, recursion depth and reuse modulate quantization sensitivity, with the deepest trained model we test (EqR) the most sensitive. Yet blockwise scaling overcomes this vulnerability, staying robust across the evaluated recursive models and transferring to the open-ended ARC-AGI benchmark.