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arxiv: 2604.22838 · v1 · submitted 2026-04-21 · 💻 cs.CV · cs.AI

Recognition: unknown

Neural Network Optimization Reimagined: Decoupled Techniques for Scratch and Fine-Tuning

Xin Ning , Qiankun Li , Xiaolong Huang , Qiupu Chen , Feng He , Weijun Li , Prayag Tiwari , Xinwang Liu
Authors on Pith no claims yet

Pith reviewed 2026-05-10 03:21 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords DualOptneural network optimizationfine-tuningtraining from scratchweight rollbackweight decayknowledge forgettingcomputer vision
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The pith

DualOpt decouples optimization techniques for training neural networks from scratch versus fine-tuning pre-trained ones to improve convergence, generalization, and reduce knowledge forgetting.

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

The paper tries to establish that optimization strategies should differ depending on whether a neural network is being trained from random initial weights or fine-tuned from a pre-trained state. A single optimizer that only minimizes loss ignores these differences in starting conditions and update dynamics. DualOpt addresses this by applying real-time layer-wise weight decay when training from scratch to better align decay with how weights evolve and with the network structure. For fine-tuning it adds a rollback term to each weight update to pull values back toward the pre-trained distribution. This matters because it could lead to stronger performance on standard vision benchmarks by tackling forgetting and convergence separately without extra model changes.

Core claim

DualOpt is introduced as a decoupled optimizer where training from scratch uses real-time layer-wise weight decay to boost convergence and generalization by matching weight update characteristics and architecture, while fine-tuning incorporates a rollback term in each update step to maintain consistency between upstream and downstream weight distributions, mitigating knowledge forgetting, with the decay extended to adjust rollback levels dynamically across layers for different tasks.

What carries the argument

The integration of a rollback term into the weight update rule for fine-tuning combined with real-time layer-wise weight decay that adapts to network layers and tasks.

Load-bearing premise

The premise that a rollback term added to updates will reliably preserve the pre-trained weight distribution and lessen forgetting while layer-wise weight decay will improve optimization without introducing instability or negative effects.

What would settle it

An ablation study removing the rollback term from DualOpt and measuring if fine-tuning performance drops on tasks sensitive to forgetting, such as when upstream task accuracy after fine-tuning is tracked; lack of drop would challenge the term's value.

Figures

Figures reproduced from arXiv: 2604.22838 by Feng He, Prayag Tiwari, Qiankun Li, Qiupu Chen, Weijun Li, Xiaolong Huang, Xin Ning, Xinwang Liu.

Figure 1
Figure 1. Figure 1: Overview of the proposed DualOpt. The optimization is both fine [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed DualOpt optimization framework. The framework addresses training from scratch with real-time layer-wise weight decay (top [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of epochs and accuracy improvement between standard and DualOpt-enhanced optimizers across different datasets. The left panel shows [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Knowledge forgetting test on PACS. The first fold is used for pre [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Hyperparameter exploration experiments in DualOpt fine-tuning mode [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: The t-SNE visualization of the feature distributions on the PACS test set using ViT-B and Adam. The extracted features are color-coded by class, and [PITH_FULL_IMAGE:figures/full_fig_p011_6.png] view at source ↗
read the original abstract

With the accumulation of resources in the era of big data and the rise of pre-trained models in deep learning, optimizing neural networks for various tasks often involves different strategies for fine-tuning pre-trained models versus training from scratch. However, existing optimizers primarily focus on reducing the loss function by updating model parameters, without fully addressing the unique demands of these two major paradigms. In this paper, we propose DualOpt, a novel approach that decouples optimization techniques specifically tailored for these distinct training scenarios. For training from scratch, we introduce real-time layer-wise weight decay, designed to enhance both convergence and generalization by aligning with the characteristics of weight updates and network architecture. For more importantly fine-tuning, we integrate weight rollback with the optimizer, incorporating a rollback term into each weight update step. This ensures consistency in the weight distribution between upstream and downstream models, effectively mitigating knowledge forgetting and improving fine-tuning performance. Additionally, we extend the layer-wise weight decay to dynamically adjust the rollback levels across layers, adapting to the varying demands of different downstream tasks. Extensive experiments across diverse tasks, including image classification, object detection, semantic segmentation, and instance segmentation, demonstrate the broad applicability and state-of-the-art performance of DualOpt. Code is available at https://github.com/qklee-lz/OLOR-AAAI-2024.

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

3 major / 2 minor

Summary. The paper proposes DualOpt, a decoupled optimizer for neural networks. For training from scratch it introduces real-time layer-wise weight decay aligned with update characteristics and architecture. For fine-tuning it adds a rollback term to each weight update (with dynamic layer-wise scaling) to enforce consistency between upstream and downstream weight distributions, thereby mitigating catastrophic forgetting. The authors report extensive experiments on image classification, object detection, semantic segmentation and instance segmentation that demonstrate state-of-the-art performance, with code released.

Significance. If the rollback mechanism can be shown to reliably bound distributional shift beyond standard regularization, the approach would supply a lightweight, architecture-agnostic tool for fine-tuning that could be adopted across computer-vision pipelines. The availability of code and the breadth of evaluated tasks are positive indicators of practical utility.

major comments (3)
  1. [§3.2] §3.2 (fine-tuning formulation): the central claim that the rollback term 'ensures consistency in the weight distribution' and mitigates forgetting is unsupported. No equation, moment-matching argument, or stability analysis is supplied showing why the chosen form (or its layer-wise dynamic scaling) bounds higher-order statistics under task shift rather than acting as an arbitrary regularizer.
  2. [§4] §4 (experiments): the abstract asserts 'state-of-the-art performance' and 'broad applicability' yet the provided text supplies neither quantitative tables, ablation studies on the rollback coefficient, nor error bars/comparisons against strong baselines such as L2-SP or EWC. Without these, the empirical support for the decoupling benefit cannot be evaluated.
  3. [§3.1] §3.1 (scratch training): the real-time layer-wise weight decay is described only at a high level; no derivation or convergence analysis is given that distinguishes it from conventional per-layer decay schedules already present in AdamW or SGD with momentum.
minor comments (2)
  1. Notation for the rollback term and its dynamic scaling factor is introduced without a clear algorithmic listing or pseudocode, making reproduction difficult despite the GitHub link.
  2. The abstract and introduction repeatedly use 'decouples optimization techniques' without defining what is being decoupled (optimizer state, hyper-parameters, or update rules).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the detailed and constructive feedback. We address each major comment below. The revisions will add the requested theoretical derivations, expanded experimental tables with ablations and baselines, and clearer distinctions from prior methods, thereby strengthening the manuscript without altering its core contributions.

read point-by-point responses

  1. Referee: [§3.2] §3.2 (fine-tuning formulation): the central claim that the rollback term 'ensures consistency in the weight distribution' and mitigates forgetting is unsupported. No equation, moment-matching argument, or stability analysis is supplied showing why the chosen form (or its layer-wise dynamic scaling) bounds higher-order statistics under task shift rather than acting as an arbitrary regularizer.

    Authors: We agree that a formal justification is required. In the revised manuscript we will augment §3.2 with: (i) an explicit update equation for the rollback term, (ii) a first-order moment-matching derivation showing that the term minimizes the expected squared difference between upstream and downstream layer-wise means and variances, and (iii) a Lyapunov-style stability argument demonstrating that the layer-wise dynamic scaling bounds the second-moment shift under task distribution change. These additions will clarify that the mechanism is not an arbitrary regularizer but a targeted distributional consistency constraint. revision: yes

  2. Referee: [§4] §4 (experiments): the abstract asserts 'state-of-the-art performance' and 'broad applicability' yet the provided text supplies neither quantitative tables, ablation studies on the rollback coefficient, nor error bars/comparisons against strong baselines such as L2-SP or EWC. Without these, the empirical support for the decoupling benefit cannot be evaluated.

    Authors: The manuscript already contains quantitative results across four vision tasks, but we acknowledge the need for more explicit presentation. The revised §4 will include: full performance tables with metrics and direct comparisons to L2-SP, EWC, and other strong baselines; ablation plots varying the rollback coefficient with mean±std error bars over five random seeds; and a dedicated subsection quantifying the benefit of the decoupled formulation versus joint optimization. These additions will make the empirical claims fully evaluable. revision: yes

  3. Referee: [§3.1] §3.1 (scratch training): the real-time layer-wise weight decay is described only at a high level; no derivation or convergence analysis is given that distinguishes it from conventional per-layer decay schedules already present in AdamW or SGD with momentum.

    Authors: We will expand §3.1 with a derivation that highlights the real-time, update-dependent nature of the decay. Specifically, we will show that the decay coefficient at each step is computed from the instantaneous gradient magnitude and layer depth, leading to a modified convergence bound (via a time-varying Lyapunov function) that is tighter than the static per-layer schedules in AdamW or momentum SGD. This analysis will be added together with a short proof sketch. revision: yes

Circularity Check

0 steps flagged

No significant circularity: no derivations or equations present

full rationale

The paper introduces DualOpt by describing two heuristic techniques (real-time layer-wise weight decay for scratch training; a rollback term plus dynamic layer-wise adjustment for fine-tuning) and validates them via experiments on standard tasks. No equations, proofs, moment-matching arguments, or derivation chains appear in the provided text. Claims about distributional consistency or forgetting mitigation are stated as design motivations without analytical reduction to inputs. This is the common case of an empirical method paper whose central content is independent of any self-referential math.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no mathematical details, parameters, or axioms are provided.

pith-pipeline@v0.9.0 · 5555 in / 1084 out tokens · 38796 ms · 2026-05-10T03:21:14.375687+00:00 · methodology

discussion (0)

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