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arxiv: 2509.18150 · v2 · pith:CMW2D6KRnew · submitted 2025-09-16 · 💻 cs.LG · cs.AI

Improving MLLM Training Efficiency via Stage-Aware Sparsity

Kean Shi , Liang Chen , Haozhe Zhao , Baobao Chang This is my paper

Pith reviewed 2026-05-21 21:57 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords multimodal large language modelstraining efficiencysparse trainingstage-awarevisual tokenslayer skippingmodality alignmentinstruction tuning
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The pith

A stage-aware sparsity scheme trains MLLMs more efficiently by using different compression techniques at different training phases.

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

The paper aims to show that redundancy in MLLM training varies by stage, so a uniform sparsity approach is suboptimal. Instead, it proposes the Sparse Training Scheme that applies visual token compression during initial modality alignment to reduce input length and dynamic layer skipping during later instruction tuning to avoid unnecessary computations. This matters for practitioners because full training of these models requires significant compute resources, and stage-specific adjustments could lower those costs while preserving accuracy. Evaluations on standard benchmarks support that the method works across different model architectures. If correct, it suggests that training processes can be optimized by observing and adapting to phase-specific inefficiencies rather than applying blanket reductions.

Core claim

The Sparse Training Scheme (STS) adapts sparsity to different sources of redundancy in MLLM training: the Visual Token Compressor reduces visual token load during modality alignment, and the Layer Dynamic Skipper dynamically skips layers during instruction tuning, leading to improved training efficiency without substantial loss in performance as verified on multiple benchmarks.

What carries the argument

The stage-aware design of the Sparse Training Scheme (STS), which switches between visual token compression and dynamic layer skipping depending on the training stage to target varying redundancies.

If this is right

  • Lowers the computational burden of processing long multimodal sequences in early training.
  • Reduces inter-layer computation overhead in later training stages.
  • Applies broadly to various MLLM architectures.
  • Preserves model performance across evaluated benchmarks.

Where Pith is reading between the lines

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

  • This could encourage development of adaptive training schedulers that monitor redundancy in real time.
  • Similar stage-aware sparsity might apply to training other large models like standard LLMs or vision transformers.
  • Future work could explore combining these components or automating the stage transitions.

Load-bearing premise

That the redundancy in training can be divided cleanly into separate stages where each sparsity technique can be applied independently without harming the model's final capabilities or slowing convergence.

What would settle it

Running full training versus STS on the same MLLM architecture and dataset, then comparing final benchmark scores and total training time; if scores drop significantly while time savings are minimal, the claim would be weakened.

read the original abstract

Multimodal Large Language Models (MLLMs) have demonstrated outstanding performance across a variety of domains. However, training MLLMs is often inefficient, as much of the computation is redundant due to the long input sequences from multimodal data and underutilized inter-layer operations. Notably, such redundancy is not static but varies across different stages of training. Building on this observation, we shift the focus to the training process itself and propose a training-efficient framework based on sparse representations, termed the Sparse Training Scheme (STS). Instead of applying a uniform sparsity strategy, STS adopts a stage-aware design that adapts to different sources of redundancy during training. Specifically, the framework consists of two complementary components: the Visual Token Compressor, which reduces the information load by compressing visual tokens during modality alignment, and the Layer Dynamic Skipper, which mitigates computational overhead by dynamically skipping unnecessary layers during instruction tuning. Our approach is broadly applicable to diverse MLLM architectures and has been extensively evaluated on multiple benchmarks, demonstrating its effectiveness and efficiency.

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.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes the Sparse Training Scheme (STS) for improving training efficiency of Multimodal Large Language Models (MLLMs). It identifies that computational redundancy varies across training stages and introduces a stage-aware framework with two components: the Visual Token Compressor, applied during modality alignment to reduce visual token information load, and the Layer Dynamic Skipper, used during instruction tuning to dynamically skip underutilized layers. The approach is described as architecture-agnostic and is asserted to have been extensively evaluated on multiple benchmarks, showing gains in efficiency while preserving effectiveness.

Significance. If the stage-aware sparsity components deliver substantial compute reductions without degrading final model quality or convergence speed, the work would offer a practical contribution to efficient MLLM training. Targeting distinct redundancy sources at different stages could reduce the resource barrier for scaling multimodal models, provided the claimed orthogonality holds under rigorous testing.

major comments (2)
  1. [Abstract] Abstract: The central efficiency claim rests on 'extensive evaluation on multiple benchmarks demonstrating its effectiveness and efficiency,' yet the abstract (and evaluation summary) supplies no quantitative metrics, per-stage redundancy statistics, ablation results, or baseline comparisons. Without these, the magnitude of claimed savings and any performance trade-offs cannot be assessed.
  2. [Method (STS components)] STS framework description: The design assumes visual-token redundancy dominates only in modality alignment and layer redundancy only in instruction tuning, with the two components remaining non-interfering. No per-stage redundancy profiles, cross-stage ablation experiments, or analysis of whether early compression alters subsequent layer-utilization statistics are provided to substantiate this separability assumption.
minor comments (2)
  1. [Visual Token Compressor] Clarify the precise compression mechanism in the Visual Token Compressor (e.g., which token features are retained and the exact reduction ratio).
  2. [Layer Dynamic Skipper] Define the decision criterion and threshold for the Layer Dynamic Skipper more explicitly, including how 'unnecessary' layers are identified at runtime.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the thoughtful and constructive comments on our manuscript. We address each major comment point by point below, indicating where revisions will be made to strengthen the presentation of our results and assumptions.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central efficiency claim rests on 'extensive evaluation on multiple benchmarks demonstrating its effectiveness and efficiency,' yet the abstract (and evaluation summary) supplies no quantitative metrics, per-stage redundancy statistics, ablation results, or baseline comparisons. Without these, the magnitude of claimed savings and any performance trade-offs cannot be assessed.

    Authors: We agree that the abstract would be strengthened by including specific quantitative metrics. The full manuscript reports detailed efficiency gains (e.g., training FLOPs and wall-clock time reductions) and performance preservation across benchmarks, along with ablations and baseline comparisons in the experimental section. In the revised manuscript we will update the abstract to explicitly state key results, such as the observed compute savings and accuracy retention, while directing readers to the corresponding tables and figures. revision: yes

  2. Referee: [Method (STS components)] STS framework description: The design assumes visual-token redundancy dominates only in modality alignment and layer redundancy only in instruction tuning, with the two components remaining non-interfering. No per-stage redundancy profiles, cross-stage ablation experiments, or analysis of whether early compression alters subsequent layer-utilization statistics are provided to substantiate this separability assumption.

    Authors: The referee correctly identifies that the manuscript would benefit from more direct evidence for the stage-specific redundancy assumptions and their non-interference. While our main results demonstrate that each component is most effective in its assigned stage and that joint application produces additive improvements, we did not include explicit per-stage redundancy profiles or cross-stage interaction analyses. We will add a dedicated subsection presenting observed redundancy statistics across training stages and additional ablation experiments that measure the effect of early visual-token compression on subsequent layer-utilization patterns. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical engineering framework without self-referential derivations or fitted predictions

full rationale

The paper proposes the Sparse Training Scheme (STS) as a practical, stage-aware sparsity framework with two components (Visual Token Compressor and Layer Dynamic Skipper) motivated by the observation that redundancy sources differ across training phases. No equations, first-principles derivations, or quantitative predictions are presented that reduce by construction to the method's own inputs, fitted parameters, or prior self-citations. The design is justified by engineering observations and evaluated empirically on benchmarks rather than through any closed theoretical loop, uniqueness theorem, or ansatz imported from the authors' own prior work. This is the most common honest finding for applied efficiency papers that do not claim mathematical derivations.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The approach rests on the domain assumption that redundancy is stage-dependent and separable, plus two newly introduced components whose independent evidence is not supplied in the abstract.

axioms (1)
  • domain assumption Redundancy in computation is not static but varies across different stages of training.
    Explicitly stated as the observation that motivates the stage-aware design.
invented entities (2)
  • Visual Token Compressor no independent evidence
    purpose: Reduces information load by compressing visual tokens during modality alignment.
    New module introduced to address early-stage redundancy.
  • Layer Dynamic Skipper no independent evidence
    purpose: Mitigates computational overhead by dynamically skipping unnecessary layers during instruction tuning.
    New module introduced to address later-stage redundancy.

pith-pipeline@v0.9.0 · 5710 in / 1268 out tokens · 29679 ms · 2026-05-21T21:57:29.118039+00:00 · methodology

discussion (0)

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Reference graph

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    Improving MLLM Training Efficiency via Stage-Aware Sparsity

    METHOD In this section, we first offer a brief introduction of naive training scheme in Sec 2.1. As shown in Figure 1, our proposed STS in- cludes two key components: the VTC and the LDS. They are de- tailed in Sec 2.2 and Sec 2.3 respectively. Finally, we demonstrate the application of STS in the practical training of MLLMs in Sec- tion 2.4. 2.1. Prelimi...

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