Recover-LoRA for Aggressive Quantization: Reclaiming Accuracy in 2-Bit Language Models via Low-Rank Adaptation with Knowledge Distillation on Synthetic Data

In three linesRecover-LoRA extends a data-free accuracy recovery method to 2-bit quantized LLMs. A mixed-precision strategy selectively quantizes MLP gate/up layers to W2 while keeping others at W4, achieving 7.5–23.3% throughput gains. Low-rank adapters trained via logit distillation on synthetic data recover 80–95% accuracy on Qwen3-4B using only 10k samples.

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