arxiv: 2510.27359 · v2 · submitted 2025-10-31 · 💻 cs.CV · cs.LG

GD-FPS: Growth-Driven Feedforward Parameter Selection for Efficient Fine-Tuning

Kenneth Yang , Wen-Li Wei , Jen-Chun Lin This is my paper

Pith reviewed 2026-05-18 02:54 UTC · model grok-4.3

classification 💻 cs.CV cs.LG

keywords parameter-efficient fine-tuninggradient-free selectionactivation growthforward passesvision modelsmemory efficiencymodel adaptation

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The pith

Relative activation growth in forward passes can replace gradients for selecting parameters to fine-tune pre-trained models.

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

The paper introduces Growth-Driven Feedforward Parameter Selection to adapt large pre-trained vision models to downstream tasks. It measures relative activation growth against a pre-training anchor using only forward passes to rank and select which parameters to update. This stands in contrast to gradient-based methods that require backward passes and thus consume more memory and time. A sympathetic reader would care because the approach promises to make fine-tuning large models feasible on hardware with tighter memory limits while delivering comparable results on image classification and segmentation.

Core claim

GD-FPS identifies the optimal parameter subset by scaling intrinsic weight magnitudes by their relative activation growth against a pre-training anchor, operating entirely via forward passes without gradients or task-specific labels.

What carries the argument

Relative activation growth measured in forward passes against a pre-training anchor, which scales weight magnitudes to rank parameters for selection.

If this is right

GD-FPS achieves competitive or superior performance over state-of-the-art PEFT baselines on 26 visual tasks spanning image classification and semantic segmentation.
It reduces peak memory usage by nearly 18 times compared to GPS during the parameter selection stage.
It accelerates execution by over 2.7 times during the parameter selection stage compared to GPS.
It guarantees deterministic selection that does not depend on stochastic batch sampling.

Where Pith is reading between the lines

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

The same forward-pass growth signal could be tested for selecting parameters in non-vision domains such as language model adaptation.
Low memory requirements might allow fine-tuning on edge devices where full gradient computations are impractical.
One could check whether the selected parameter sets remain effective when the downstream task distribution differs sharply from pre-training data.

Load-bearing premise

Relative activation growth against a pre-training anchor serves as a reliable proxy for identifying which parameters will improve performance after fine-tuning on new tasks.

What would settle it

If fine-tuning with parameters chosen by GD-FPS yields substantially lower accuracy than those chosen by gradient-based selection on the same visual tasks, the core claim would not hold.

Figures

Figures reproduced from arXiv: 2510.27359 by Jen-Chun Lin, Kenneth Yang, Wen-Li Wei.

**Figure 2.** Figure 2: (a) Peak GPU memory usage and (b) Parameter selection latency on the FGVC benchmark. Method Dataset Natural Specialized Structured VTAB CIFAR-100 Caltech101 DTD Flowers102 Pets SVHN Sun397 Patch Camelyon EuroSAT Resisc45 Retinopathy Clevr/count Clevr/distance DMLab KITTI/distance dSprites/loc dSprites/ori SmallNORB/azi SmallNORB/ele Mean Acc. Mean Params. (%) Full Fine-Tuning [16] 68.9 87.7 64.3 97.2 86.9 … view at source ↗

read the original abstract

Parameter-Efficient Fine-Tuning (PEFT) has emerged as a key strategy for adapting large-scale pre-trained models to downstream tasks, but existing approaches face notable limitations. Addition-based methods, such as Adapters, introduce inference latency and engineering complexity, whereas selection-based methods like Gradient-based Parameter Selection (GPS) require a full backward pass. The reliance on gradients not only incurs massive memory usage and substantial computational latency, but also leaves the selection vulnerable to the randomness of stochastic batch sampling. To resolve this, we propose Growth-Driven Feedforward Parameter Selection (GD-FPS). Operating entirely via forward passes, this strictly gradient-free method identifies the optimal parameter subset by scaling intrinsic weight magnitudes by their relative activation growth against a pre-training anchor. Evaluated on $26$ visual tasks spanning image classification and semantic segmentation, GD-FPS achieves competitive or superior performance over state-of-the-art PEFT baselines. Crucially, compared to GPS, it reduces peak memory usage by nearly $18\times$ and accelerates execution by over $2.7\times$ during the parameter selection stage. By guaranteeing deterministic selection, GD-FPS offers a memory-efficient, fast, and robust solution for fine-tuning.

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

GD-FPS replaces gradient-based parameter selection with a forward-pass activation growth measure, delivering clear memory and speed wins over GPS while keeping performance competitive on the reported tasks.

read the letter

The main thing to know is that this paper gives a straightforward gradient-free way to pick which parameters to fine-tune in large vision models. Instead of running a backward pass like GPS, they measure how much activations grow on downstream data relative to a pre-training anchor, then scale the original weight magnitudes by that factor to decide the subset. This keeps selection deterministic and avoids the memory spike from gradients or the randomness of stochastic batches.

Referee Report

2 major / 3 minor

Summary. The manuscript proposes Growth-Driven Feedforward Parameter Selection (GD-FPS), a strictly gradient-free method for identifying a subset of parameters to fine-tune in large pre-trained vision models. GD-FPS computes relative activation growth via forward passes on downstream data against a pre-training anchor, scales intrinsic weight magnitudes by these growth factors, and selects the top parameters for subsequent fine-tuning. The authors evaluate the approach on 26 visual tasks spanning image classification and semantic segmentation, claiming competitive or superior performance relative to state-of-the-art PEFT baselines while achieving nearly 18× lower peak memory and 2.7× faster execution during the selection stage compared with Gradient-based Parameter Selection (GPS), plus deterministic selection.

Significance. If the reported results and the underlying proxy hold under scrutiny, the work provides a practical route to memory-efficient and deterministic parameter selection for PEFT without back-propagation. The efficiency gains (memory and speed) and removal of stochastic batch effects could be valuable for adapting very large models in constrained environments. The approach is notable for operating entirely in the forward pass, which is a clear departure from gradient-dependent selection methods.

major comments (2)

[§3] §3 (Method description): The precise mathematical definition of relative activation growth, the scaling operation applied to weight magnitudes, and the selection threshold (or any scaling factor) are not provided with equations or pseudocode. Without these details the method cannot be reproduced and the claim of a 'parameter-free' or fully deterministic procedure cannot be verified.
[§4–5] §4–5 (Experiments and ablations): No correlation analysis, ablation swapping the GD-FPS mask for a gradient-norm mask, or sensitivity study to the pre-training anchor is reported. Given that the central claim rests on activation growth serving as a reliable proxy for downstream loss reduction without labels or gradients, the absence of such checks leaves the validity of the proxy untested and load-bearing for the performance claims.

minor comments (3)

[Abstract / §1] The abstract and introduction cite '26 visual tasks' but do not list the exact datasets or splits used; a table or appendix reference would improve clarity.
[Figures] Figure captions and axis labels in the efficiency comparison plots should explicitly state the model sizes and batch sizes used for the memory and timing measurements.
[§2] A few sentences in §2 on related work could more explicitly contrast GD-FPS with other forward-pass-only selection heuristics that have appeared in the PEFT literature.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments highlight important aspects of clarity and validation that we have addressed through revisions. We respond to each major comment below.

read point-by-point responses

Referee: [§3] §3 (Method description): The precise mathematical definition of relative activation growth, the scaling operation applied to weight magnitudes, and the selection threshold (or any scaling factor) are not provided with equations or pseudocode. Without these details the method cannot be reproduced and the claim of a 'parameter-free' or fully deterministic procedure cannot be verified.

Authors: We agree that the original §3 did not provide sufficient mathematical detail for reproducibility. In the revised manuscript we have added explicit equations: relative activation growth is defined as g(w) = (||a_down(w)|| - ||a_pre(w)||) / ||a_pre(w)|| where a_down and a_pre denote forward-pass activations on downstream and pre-training anchor data; the scaled score is s(w) = |w| · g(w); and selection retains the top-k parameters by s(w) with no additional thresholds or scaling factors. We have also inserted Algorithm 1 containing the complete pseudocode for the forward-only procedure. These additions confirm the method is fully deterministic and parameter-free while enabling direct implementation. revision: yes
Referee: [§4–5] §4–5 (Experiments and ablations): No correlation analysis, ablation swapping the GD-FPS mask for a gradient-norm mask, or sensitivity study to the pre-training anchor is reported. Given that the central claim rests on activation growth serving as a reliable proxy for downstream loss reduction without labels or gradients, the absence of such checks leaves the validity of the proxy untested and load-bearing for the performance claims.

Authors: The referee is correct that direct validation of the activation-growth proxy would strengthen the central claim. While the results on 26 tasks already provide strong empirical evidence, we have added the requested analyses in the revision: (i) a correlation study (Spearman rank) between GD-FPS scores and gradient-norm importance on a representative subset of tasks, (ii) an ablation in §5 that substitutes the GD-FPS mask with a pure gradient-norm mask and reports the resulting fine-tuning performance, and (iii) a sensitivity study varying the pre-training anchor (different data subsets and checkpoints). These new results are now included in §4 and §5 and demonstrate that the proxy is both correlated with gradient-based importance and robust to anchor choice. revision: yes

Circularity Check

0 steps flagged

No significant circularity: GD-FPS is a heuristic selection rule defined by forward-pass measurements, not a derivation that reduces to its inputs.

full rationale

The paper defines GD-FPS directly as scaling weight magnitudes by relative activation growth computed in forward passes against a pre-training anchor. This is an explicit, task-agnostic heuristic rather than a claimed first-principles derivation or prediction that collapses to fitted parameters or self-citations. Empirical results on 26 tasks are presented as validation of the proxy, not as outputs forced by construction from the selection rule itself. No load-bearing self-citation chains, uniqueness theorems, or renaming of known results appear in the provided description. The method is self-contained as a proposed alternative to gradient-based selection.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Abstract-only review means free parameters and exact axioms are not visible; the core assumption that activation growth proxies importance is treated as a domain assumption.

free parameters (1)

selection threshold or scaling factor
Likely required to decide which scaled parameters to keep, but not quantified in the abstract.

axioms (1)

domain assumption Activation growth relative to pre-training anchor indicates parameter importance for fine-tuning
Central premise invoked when the method replaces gradient-based selection.

pith-pipeline@v0.9.0 · 5742 in / 1139 out tokens · 32124 ms · 2026-05-18T02:54:37.465575+00:00 · methodology

discussion (0)

Reference graph

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