SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

Haihao Shen; Heng Guo; Weiwei Zhang; Wenhua Cheng; Zaner Ma

arxiv: 2512.04746 · v2 · pith:J63V3UCTnew · submitted 2025-12-04 · 💻 cs.CL · cs.AI

SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs

Wenhua Cheng , Weiwei Zhang , Heng Guo , Haihao Shen , Zaner Ma This is my paper

Pith reviewed 2026-05-21 17:00 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords post-training quantizationlarge language modelsmixed precisionbit allocationlow-bit compressionquantization errorsmodel deployment

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The pith

An adaptive strategy for assigning bit widths per layer narrows the accuracy gap in extremely low-bit LLM quantization to roughly 1 percent at 4.5 bits on average.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents a post-training quantization approach for large language models that maintains performance even at very low bit widths. It uses an adaptive strategy to assign different bit precisions to different layers based on gradient information and the errors introduced by quantization. Additional stabilization methods help improve the results in challenging low-bit scenarios. If this works as claimed, it would mean that compressed models can run with almost the same quality as the original on hardware with limited resources. Readers would care because it addresses a key barrier to using advanced AI models efficiently.

Core claim

The central discovery is that guiding layer-wise bit allocation with gradient information and quantization-induced reconstruction errors, combined with loss filtering and pre-tuning scale search, allows near-lossless performance in mixed MXFP settings narrowing the gap to approximately 1 percent at an average of 4.5 bits, while also improving accuracy in 2-bit weight-only quantization for various large language models.

What carries the argument

Adaptive mixed-precision bit allocation that leverages gradient information and reconstruction errors to decide precision per layer.

If this is right

Quantized large language models suffer only about 1 percent accuracy loss under mixed precision at an average of 4.5 bits.
Performance in 2-bit weight-only quantization improves substantially compared with earlier techniques.
The method maintains its benefits across a variety of different large language models.
Lightweight stabilization allows effective tuning even when bit widths drop to the most aggressive levels.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same signals used for bit allocation could guide other compression steps such as selective pruning.
Edge devices might achieve lower power draw when running models with the resulting per-layer bit patterns.
The stabilization steps could be examined for use when training models with quantization constraints from the start.

Load-bearing premise

That gradient information combined with quantization reconstruction errors supplies a reliable non-overfitting signal for choosing bit allocations per layer that generalizes across models and tasks.

What would settle it

Testing the quantization procedure on a large language model outside the original experiments and measuring whether accuracy at 4.5-bit average mixed precision stays within 1 percent of the full-precision baseline.

Figures

Figures reproduced from arXiv: 2512.04746 by Haihao Shen, Heng Guo, Weiwei Zhang, Wenhua Cheng, Zaner Ma.

**Figure 2.** Figure 2: Layer-wise DeltaLoss sensitivity of Llama-3.1-8B-Instruct under W2A16 and MXFP4. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

read the original abstract

Extremely low-bit quantization is critical for efficiently deploying Large Language Models (LLMs), yet it often leads to severe performance degradation at 2 bits and even at 4 bits (e.g., MXFP4). We present SignRoundV2, a post-training quantization framework designed to maintain high performance even under aggressive compression. SignRoundV2 introduces (1) a simple yet efficient adaptive mixed-precision strategy that leverages gradient information and quantization-induced reconstruction errors to guide layer-wise bit allocation, and (2) a set of lightweight stabilization techniques, including loss filtering and a pre-tuning scale search, to improve tuning effectiveness in extremely low-bit regimes. Our approach takes a significant step toward closing the performance gap between quantized and full-precision models. Experimental results across diverse LLMs demonstrate that SignRoundV2 achieves near-lossless performance in mixed MXFP settings, narrowing the gap to $\sim$1\% at an average of 4.5 bits, while substantially improving accuracy in challenging 2-bit weight-only quantization. The source code is available at \url{https://github.com/intel/auto-round}.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

SignRoundV2 pairs gradient-plus-reconstruction-error bit allocation with basic stabilization tricks, but the reported gains rest on thin experimental detail and may not generalize cleanly.

read the letter

The core contribution is an adaptive mixed-precision scheme that uses per-layer gradients and quantization reconstruction error to pick bit widths, plus two lightweight stabilizers: loss filtering and a pre-tuning scale search. They claim this gets within ~1% of full precision at 4.5-bit average MXFP and lifts 2-bit weight-only results noticeably across several LLMs, with code released on GitHub. That combination of signals for allocation is presented as new relative to prior PTQ work, and the stabilization steps address a known pain point in very low-bit tuning. Releasing code is the clearest practical value here; anyone running inference experiments can test the allocator directly. The paper stays empirical and benchmark-driven, which fits the post-training quantization literature. The main weakness is that the abstract (and presumably the early sections) gives headline numbers without visible controls, ablation tables, or checks on whether the chosen allocations transfer to a fresh calibration distribution or different downstream tasks. In 2-bit and mixed low-bit regimes the error surface is jagged, so gradient magnitudes and reconstruction errors can be dominated by a few outlier layers; any method that turns those directly into discrete decisions risks fitting the calibration set idiosyncrasies. If the full paper shows that the allocations remain stable across multiple calibration draws and that the accuracy lift survives when the same allocator is applied to held-out models or tasks, the central claim strengthens. Otherwise the numbers stay tied to the specific experimental choices. This is the kind of incremental but usable engineering paper that belongs in a compression or efficient-inference venue. A reader who already works on PTQ or LLM deployment would find the allocator and the released code worth examining. It is coherent enough and grounded enough in an active area to merit real referee time rather than a desk reject, even if the revisions will likely focus on generalization checks and fuller ablations.

Referee Report

1 major / 2 minor

Summary. The paper introduces SignRoundV2, a post-training quantization framework for LLMs that uses an adaptive mixed-precision strategy leveraging gradient information and quantization-induced reconstruction errors for layer-wise bit allocation, together with stabilization techniques including loss filtering and pre-tuning scale search. It reports near-lossless performance in mixed MXFP settings (gap narrowed to ~1% at 4.5 bits average) and substantial accuracy gains in challenging 2-bit weight-only quantization across diverse LLMs, with code released publicly.

Significance. If the adaptive allocation proves robust, the work would meaningfully advance extremely low-bit PTQ for LLMs by reducing the accuracy gap to full precision while supporting efficient deployment; the public code release aids reproducibility and is a clear strength.

major comments (1)

[Experimental Results] Experimental Results section: The central claims of near-lossless ~1% gap at 4.5-bit mixed MXFP and substantial 2-bit improvements rest on the layer-wise bit allocation driven by gradients plus reconstruction error. No experiments are reported that test whether this allocation transfers to a fresh calibration distribution or different downstream tasks, which is load-bearing because the reconstruction error surface is highly non-convex in 2-bit regimes and gradients can be dominated by outliers.

minor comments (2)

[Abstract] Abstract: The specific LLMs, exact mixed MXFP configurations, and baseline methods compared should be named to allow immediate assessment of the reported numbers.
The manuscript would benefit from an explicit statement of the calibration dataset size and composition used for the bit-allocation decisions.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review. The comment on experimental validation of the bit allocation is well-taken, and we address it directly below while committing to strengthen the manuscript accordingly.

read point-by-point responses

Referee: [Experimental Results] Experimental Results section: The central claims of near-lossless ~1% gap at 4.5-bit mixed MXFP and substantial 2-bit improvements rest on the layer-wise bit allocation driven by gradients plus reconstruction error. No experiments are reported that test whether this allocation transfers to a fresh calibration distribution or different downstream tasks, which is load-bearing because the reconstruction error surface is highly non-convex in 2-bit regimes and gradients can be dominated by outliers.

Authors: We appreciate the referee highlighting the importance of transferability for the adaptive allocation. Our bit allocation is computed once per model using gradients and per-layer reconstruction errors on a standard 128-sample calibration set from WikiText-2, after which the fixed allocation is applied and the model is evaluated on held-out perplexity benchmarks (WikiText-2, C4) as well as a broad suite of zero-shot downstream tasks (ARC, HellaSwag, PIQA, Winogrande, etc.). This already demonstrates that the resulting quantized models generalize across tasks. We agree, however, that an explicit test of allocation stability under a changed calibration distribution would further support the claims, especially given the non-convexity concerns at 2 bits. In the revised manuscript we will therefore add a new subsection reporting bit allocations and end-to-end accuracy when the calibration set is replaced by an equal-sized sample from C4; we will also include a brief analysis of allocation variance across these distributions. Our stabilization techniques (loss filtering and pre-tuning scale search) are intended to reduce sensitivity to outliers and non-convexity during the per-layer optimization, but we will make this connection more explicit in the revision. revision: yes

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

The method rests on empirical tuning rather than new theoretical axioms or invented physical entities.

free parameters (1)

layer-wise bit allocation thresholds
Decisions on per-layer precision are driven by computed gradients and errors and are therefore fitted or searched during the process.

pith-pipeline@v0.9.0 · 5739 in / 1098 out tokens · 61369 ms · 2026-05-21T17:00:36.905367+00:00 · methodology

discussion (0)

Forward citations

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Reference graph

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