Selective Underperformance via Fine-Tuning

Updated 31 August 2025

Selective underperformance via fine-tuning is a modular adaptation strategy that updates only targeted filters, layers, or data samples to reduce computation and mitigate overfitting.
It employs methods like filter ranking, gradient analysis, and evolutionary optimization to select and update only the most pertinent components during domain adaptation.
This approach enhances performance in vision, language, and federated learning scenarios while balancing robustness with resource constraints.

Selective underperformance via fine-tuning refers to the intentional or emergent phenomenon where only a subset of model components—such as parameters, layers, modules, or data—are updated or prioritized during domain adaptation, task transfer, or targeted model modification. Recent research has developed a variety of methodologies for selective fine-tuning, driven by goals including computational efficiency, improved robustness, domain-specific adaptation, resource constraints, regularization, and the mitigation of over- or underfitting. These strategies span applications in vision, language, federated learning, model editing, and multi-objective retrieval, and are underpinned by technical mechanisms ranging from filter-level ranking and gradient-based selection to evolutionary optimization and structured masking.

1. Fundamental Concepts and Selective Fine-Tuning Paradigms

Selective fine-tuning fundamentally departs from full-model adaptation by identifying and updating only model components deemed most relevant, effective, or susceptible to transfer-induced underperformance. Core variants include:

Filter-level selectivity: Fine-tune only those convolutional filters in a CNN whose activations are most perturbed by domain shift or input corruption, measured via metrics such as Earth Mover’s Distance (EMD) on activation maps, and ranked with aggregation schemes like the Borda count (Bianchi et al., 2019).
Unit/layer selection: Automatically select a single or limited set of layers or blocks for fine-tuning, often by optimizing held-out validation performance post-adaptation (see flex-tuning) (Royer et al., 2020), or by ranking with Fisher Information Matrix (FIM) scores (Lodha et al., 2023).
Selective parameter/adaptation block activation: Utilize scoring functions and indicator masks to activate only a fraction of LoRA or adapter blocks during adaptation, thereby controlling both the scale and locality of parameter updates (Bafghi et al., 26 Jan 2025, Son et al., 26 Nov 2024).
Sample/data selection: Employ data or token selection strategies, guided by private data or efficiency objectives, to constrain adaptation to the most domain-relevant or informative samples (Yu et al., 2023, Yin et al., 21 Oct 2024).
Optimization of layer-wise or patch-wise transfer: Use evolutionary algorithms to determine which network blocks to update and at what learning rates, encoding such selective adaptation in genotype vectors (Colan et al., 21 Aug 2025).

Selective underperformance, in this context, refers to the model’s limited or localized improvement due to focusing adaptation on such targeted subsets, which may be desirable (e.g., for locality preservation in model editing) or problematic (e.g., when subset omission leads to a persistent error floor).

2. Selection Criteria and Mechanisms

Selective fine-tuning relies on robust mechanisms to quantify importance, susceptibility, or adaptation utility:

Activation-based ranking: Compute the response change for each filter, channel, or unit in response to domain shift (e.g., clean vs. noisy image pairs), using distances such as EMD, and employ consensus-based ranking (e.g., Borda count). Fine-tune only the highest-ranked (most “susceptible”) components to efficiently recover lost performance, especially in low-data regimes (Bianchi et al., 2019).
Information-theoretic metrics: Estimate the Fisher Information Matrix (FIM) for each parameter or layer to assess task-specific sensitivity, aggregating diagonals via Frobenius or L₂ norms to identify critical layers for adaptation (Lodha et al., 2023). Empirically, task relevance is often concentrated within a small subset of layers, enabling “surgical” fine-tuning.
Gradient signal analysis: In federated or distributed contexts, compute local gradient norms per layer to identify “important” components on each client. A global regularization term aligns layer selection across clients, balancing local adaptation with convergence guarantees (Sun et al., 28 Aug 2024).
Evolutionary scoring: Evolutionary or metaheuristic search frameworks encode selection and importance per block as a genotype, with evolutionary operations optimizing validation accuracy under resource constraints. Features such as selection mask thresholds and importance-weighted learning rates are updated adaptively per candidate (Colan et al., 21 Aug 2025).
Masking and gating: Binary masks or indicator functions, possibly learned and regularized (e.g., with ℓ₁ penalties or via importance metrics like CKA), dynamically determine which modules are unfrozen or updated during training (Son et al., 26 Nov 2024, Bafghi et al., 26 Jan 2025).
Sample-level judgment: Select training targets based on equivalence checks using model predictions, LLM judges, or heuristic bottom-line agreement. For generative models, rephrase gold responses with the base model to maintain output distribution alignment (Gupta et al., 7 Sep 2024, Gupta et al., 12 Feb 2025).
Utility-driven data selection: With compute-aware constraints, balance the cost of selection (perplexity, gradient, or retrieval-based scoring) against the expected training benefit. For resource-constrained finetuning, computationally cheaper selection methods can be more effective overall than high-cost “oracle” strategies (Yin et al., 21 Oct 2024).

3. Efficiency, Resource Usage, and Regularization

A primary motivation for selective fine-tuning is to reduce computational burden while preserving or improving performance—especially under limited data scenarios or strong regularization needs:

Parameter reduction: By fine-tuning only a minority of critical modules or filters, such as the top 25% of susceptible CNN filters or a handful of FIM-ranked layers, the parameter update space shrinks substantially. This leads to faster convergence and lower overfitting risk (Bianchi et al., 2019, Lodha et al., 2023).
Resource scaling: In federated learning, selective layer tuning enables clients to operate within resource budgets, though misalignment across clients (heterogeneous choices) can increase the error floor (Sun et al., 28 Aug 2024). Strategic, gradient-informed selection mitigates these inefficiencies relative to naive, fixed slicing.
Adapter/resource freezing: Gradually freezing less-important LoRA/adapters via importance scores (e.g., CKA) reduces activation memory, backward length, and TFLOPs, yielding up to 43% memory and 35% compute reduction in some tasks without degrading accuracy (Son et al., 26 Nov 2024).
Selective block activation in PEFT: Gating LoRA blocks via learnable indicator functions allows adaptation with as little as 5% of active modules. This substantially preserves zero-shot and OOD performance compared to dense adaptation, thus regularizing against catastrophic forgetting (Bafghi et al., 26 Jan 2025).
Regularization effect: Progressive freezing or selective activation of parameters imposes implicit regularization (e.g., via parameter masks), analytically yielding flatter loss landscapes (lower Hessian eigenvalues), which correlates with improved generalization and reduced sharp minima risk (Son et al., 26 Nov 2024).
Evolutionary cost-control: BioTune reduces the number of trainable parameters, sometimes freezing 70% of a network, with only marginal accuracy loss (or even improvement) across domains, highlighting the importance of layer-wise adaptation for both efficiency and generalization (Colan et al., 21 Aug 2025).

4. Selective Underperformance, Robustness, and Trade-Offs

Selective fine-tuning addresses or leverages selective underperformance in various modalities:

Robustness to distortion and adversaries: Selective adaptation recovers model robustness under noise or blur (filter-level selectivity) and mitigates adversarial vulnerability (parameter masking based on first/second-order robustness in NLP) (Bianchi et al., 2019, Jiang et al., 2022).
Editing and preservation of knowledge: In model editing, prompt-masked fine-tuning or conditional optimization target only output probabilities for the edited fact, intentionally minimizing performance change elsewhere—this constitutes selective underperformance by design, preserving locality and minimizing off-target knowledge loss (Gangadhar et al., 16 Feb 2024).
Generalization vs. specialization in LLMs: Selective self-rehearsal or self-to-supervised fine-tuning leverages model-generated correct responses (vetted by a judge) instead of gold responses. This reduces drift from pre-trained distributions and curbs over-specialization, resulting in an order-of-magnitude reduction in average performance drop across generalization benchmarks (e.g., MMLU, TruthfulQA) compared to standard SFT (Gupta et al., 7 Sep 2024, Gupta et al., 12 Feb 2025).
Multi-objective balance: In multi-objective EBR, cascading selective masking trains each objective (exposure, click, conversion) in sequence, freeing independent parameter space per objective without increasing network size or inference cost. This enables the retention of exposure performance while improving downstream objectives and serves as a template for mitigating underperformance caused by inter-objective conflicts (Deng et al., 17 Apr 2025).
Compute-aware selection: Expensive data or sample selection methods (perplexity, gradient-based) are only compute-optimal when training model size vastly exceeds the selection model; otherwise, cheaper methods (BM25, embedding) dominate. This sets a boundary for when to expect underperformance due to over-investment in selection relative to total budget (Yin et al., 21 Oct 2024).
Federated and online settings: Error terms in convergence analysis for selective-tuning in federated learning grow when important layers are omitted or when clients’ selection patterns are too heterogeneous (Sun et al., 28 Aug 2024). Online RL settings similarly expose significant early-stage underperformance when exploration is not properly coupled with stable offline guidance (Wang et al., 1 May 2025).

5. Empirical Outcomes and Applications

Empirical results across domains demonstrate the efficacy and trade-offs entailed by selective approaches:

Vision and transfer learning: Selective filter-level and evolutionary fine-tuning yields comparable or superior accuracy to full fine-tuning or existing adaptive schemes (AutoRGN, LoRA) on MNIST, CIFAR-10, FGVC-Aircraft, and other datasets, with reduced overfitting (Bianchi et al., 2019, Colan et al., 21 Aug 2025).
Language and LLM adaptation: Selective (mask-based, judge-informed) fine-tuning improves generalization on tasks such as mathematical reasoning, reading comprehension, and programming, reducing test performance drops by 2x compared to standard supervised tuning (Gupta et al., 7 Sep 2024, Gupta et al., 12 Feb 2025).
Adversarial robustness and regularization: ROSE’s parameter masking, driven by first/second-order risk, yields improvements in adversarial benchmarks (GLUE, AdvGLUE, RoBERTa) over both baseline and competing robust tuning schemes, while enhancing loss surface flatness (Jiang et al., 2022).
Multi-objective retrieval: CSMF selectively masks and retrains parameter subsets, leading to Recall@50 and nDCG@50 gains of 6–7%, with minimal online latency increase, in large-scale industrial recommender settings (Deng et al., 17 Apr 2025).
Federated learning and compute-constrained domains: Strategic selective tuning and compute-aware selection balance cost and adaptation quality across distributed clients and resource budgets, sustaining competitive performance even with drastic reduction in trainable parameters (Sun et al., 28 Aug 2024, Yin et al., 21 Oct 2024).

These results validate that targeted updates can both limit unwanted collateral changes (mitigating selective underperformance) and deliver efficiency and robustness across a range of architectures and domains.

6. Open Problems and Future Directions

Selective fine-tuning strategies introduce new research challenges and opportunities:

Joint adaptation: Moving beyond single-layer/unit selection to multi-unit or groupwise strategies could enable richer representations and further boost adaptation (Royer et al., 2020).
Cross-modal and multi-domain deployment: Extending selective protocols to other modalities (audio, video), languages, or multi-task settings poses challenges in both selection metric reliability and regularization design.
Theoretical guarantees: Establishing formal optimality or generalization bounds for selective tuning—especially as selection criteria become more sophisticated—remains largely open.
Automated selection: Improving methods to estimate or learn the importance of layers, patches, adapters, or samples, possibly in a differentiable or meta-learned framework, could lead to even more dynamic and robust adaptation.
Scalability and resource adaptation: Adapting selective strategies for extremely large models (e.g., 70B+ parameters) or privacy-sensitive/compute-constrained settings underscores the need for efficiency–accuracy trade-off analysis and resource-aware algorithms (Yu et al., 2023, Yin et al., 21 Oct 2024).
Integrative strategies: Combining selective fine-tuning with data selection, augmentation, masking, and PEFT techniques (e.g., SAFE, LoRA, DoRA) offers avenues for creating highly modular and adaptable training pipelines.

7. Summary Table: Major Selective Fine-Tuning Methods

Strategy	Selection Mechanism	Key Benefit
Filter-level (CNN)	EMD + Borda rank of filter activations	Recovers robustness to noise, less retrain
Flex-tuning	Validation-driven unit/layer selection	Avoids overfitting/underfitting in domain shift
FIM-based (NLP)	Fisher Information ranking of layers	Localizes adaptation, reduces computation
Selective Adapter	CKA-based, importance-score masking	Early freezing, regularization, resource savings
LoRA block select.	Learnable gating/indicator mask	Efficiency, OOD preservation, avoids catastrophic forgetting
Evolutionary (BioTune)	Genotype-coded layer/block and learning rate selection	Efficient, accurate, avoids overfitting
Selective Self/S3FT	LLM judge on sample-level, self/gen rephrase targets	Generalization, reduction in specialization
Data selection	Private, embedding, gradient or PPL-based	Privacy/compute constrained adaptation
Multi-Objective (CSMF)	Cascaded selective parameter masking	Avoids conflict, enables sequential multi-task learning

A general trend is the movement away from monolithic adaptation toward intelligent, computationally targeted, and often regularization-enhancing fine-tuning. The challenge of selective underperformance—both as a risk and as a strategy—is thus increasingly addressed through a diverse array of selective adaptation methods, with substantial empirical and theoretical grounding across modalities and learning settings.