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Life sciences · Preprint
arXiv · September 3, 2026
Early or partial results. Treat as a signal, not a conclusion.
Scale-QLoRA is a novel method for merging LoRA adapters into natively 4-bit quantized language models by adapting only per-block scale fields while preserving the E2M1 code plane exactly. The authors report that the method achieves accuracy matching a merge-aware QAT-LoRA baseline across four models and tasks, offers computational advantages (3.9x faster training step on an 8B model, ~125x faster scale-only task swap), and eliminates accuracy loss observed with naive merging (up to 39 percentage points).
Algorithm proposal with empirical validation across multiple models and tasks. Four language models and four downstream tasks; no human subjects or clinical populations.. Intervention: Scale-QLoRA: adapted per-block scale fields with frozen E2M1 code plane.. Compared with: Merge-aware QAT-LoRA (re-derives code plane through quantizer)..
Naive merging of LoRA into 4-bit quantized checkpoints causes up to 39 percentage point accuracy loss because reconstruction optimum is the unmodified base model. Scale-QLoRA and merge-aware QAT-LoRA are reported as accuracy-lossless across four models and four tasks, with no claimed accuracy ordering between them. Scale-QLoRA reduces per-step training time by 3.9x on the dense 8B model compared to merge-aware QAT-LoRA by eliminating the straight-through estimator step.
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This is a technical methods paper describing a novel algorithm for adapter merging in quantized language models, with empirical validation across four models and tasks, but without peer review, clinical outcomes, or comparison to established baselines sufficient to establish superiority.
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Merging a LoRA adapter into its base model is standard deployment practice: it removes the runtime adapter's per-forward overhead and leaves a single standalone checkpoint any serving stack can load. On a native 4-bit microscaling checkpoint (NVFP4, MXFP4) that step stops being free. The merged weights must be written back through a quantizer, which re-derives the checkpoint's discrete E2M1 code plane (roughly 90% of the artifact's bytes), so the deployed artifact becomes coupled to one quantization convention, and every later code-touching event in its lifecycle can move it. Done naively the step is worse than fragile: it deletes the adaptation, by up to 39 pp, because against an already-on-grid base the reconstruction optimum is that base. Scale-QLoRA instead adapts only the native per-block scale field, trains those scales on the deployment grid, and freezes every E2M1 code. Within a fixed native format, scale grid, block layout and code plane, merging is then a bit-exact identity and the merged artifact is code-invariant. Across four models and four tasks, Scale-QLoRA and merge-aware QAT-LoRA are both accuracy-lossless, so we claim no accuracy ordering between them; they differ structurally, in that QAT-LoRA re-derives the code plane through a quantizer while Scale-QLoRA preserves it exactly. That difference is what the lifecycle prices: nearest-rounding implementations disagree by about a point on the measured task, and more extreme rule mismatches can drive the weight-space artifact to ~0%, which we report as a sensitivity bound rather than a deployment frequency. Preserving the code plane also drops the weight-space straight-through estimator from training (3.9x per step on the dense 8B model) and enables exact rollback, code-plane deduplication, and a ~125x faster scale-only task swap.
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