Two-Stage Fine-Tuning Strategy

• Two-stage fine-tuning strategy is a method where model parameters are sequentially updated—first to establish global priors and then to specialize for task-specific requirements.
• The approach is applied across domains like computer vision, NLP, and time-series analysis to combat overfitting, domain discrepancies, and catastrophic forgetting.
• Empirical results indicate improvements in metrics such as top-1 accuracy and efficiency, underpinning its practical value in research and deployment.

A two-stage fine-tuning strategy is a training paradigm in which model parameters are updated through two sequential and functionally distinct phases. This approach has emerged as a powerful technique across diverse domains such as computer vision, natural language processing, time-series modeling, and multimodal learning, especially in problems where full supervision is limited, task domains differ from pretraining data, or model specialization risks overfitting or catastrophic forgetting. Two-stage fine-tuning enables hierarchical learning: an initial stage introduces global or task-oriented priors (often leveraging pretraining or attention mechanisms), while a second stage performs adaptation, discrimination, or constraint enforcement appropriate to the target task. The method has been instrumental in achieving state-of-the-art performance in various benchmarks and is associated with improved generalization, sample efficiency, and interpretability.

1. Fundamental Principles of Two-Stage Fine-Tuning

The two-stage fine-tuning concept typically consists of a preparatory phase followed by a specialization phase:

• Stage 1 (Coarse or Global Adaptation): The model adapts higher-level or global representations using either generic task data, auxiliary objectives (e.g., domain adaptation, attention localization, knowledge transfer), or large-scale but weakly/heterogeneously labeled data. Parameters updated in this stage may include either the entire backbone (in domain adaptation) or a restricted set such as prompt layers, attention modules, or final classifier layers.
• Stage 2 (Specialized or Local Adaptation): The model is further tuned—often with stricter or task-specific criteria—using fine-grained labeled data, regularization objectives, domain-specific priors, or newly collected samples. This stage encourages specialization while leveraging the robust initializations or priors from stage one.

A canonical mathematical formulation is to define a composed loss: $$\min_{\Theta} \mathcal{L}_2(\Theta) + \lambda \mathcal{L}_1(\Theta')$$ where $\mathcal{L}_1$ governs learning in stage one with $\Theta'$ (potentially a subset of $\Theta$), and $\mathcal{L}_2$ is optimized in stage two, typically with a lower learning rate or different data distribution.

This approach can be instantiated with various choices of objectives, optimization schedules, and parameter subsets, as demonstrated in methods such as Coarse2Fine (Eshratifar et al., 2019), LightPAFF (Song et al., 2020), and ProMoT (Wang et al., 2022).

2. Methodological Variants and Domain-Specific Implementations

Different research areas adapt the two-stage fine-tuning strategy to their specific needs:

• Fine-Grained Visual Classification: The Coarse2Fine system (Eshratifar et al., 2019) utilizes a coarse-stage attention-localizing network followed by a fine-grained classifier trained on soft-masked regions with a joint loss encompassing classification and attention center constraints. Feedback through a differentiable deconvolution path allows attention maps to be refined during the second stage.
• Knowledge Distillation and Compression: In LightPAFF (Song et al., 2020), knowledge distillation is executed in two stages: first, knowledge is transferred from a large teacher to a compact student model during pretraining; second, after task-specific fine-tuning of the teacher, further distillation aligns the student with the specialized outputs. This two-phase distillation is formalized as: $$L(\theta) = \sum_{(x, y)} [(1 - \lambda)\cdot\text{MLE}(x, y; \theta) + \lambda \cdot\text{KL}(P(x; \theta_T), P(x; \theta))]$$ where $\lambda$ mediates teacher supervision.
• Imbalanced Classification: For long-tailed data distributions, a first stage tunes only the final layer with a class-reweighted loss (e.g., LDAM or inverse-frequency cross-entropy), protecting minority-class representations, while a second stage unfreezes the full network for conventional fine-tuning (ValizadehAslani et al., 2022).
• Prompting and Modular NLP Pipelines: The ProMoT protocol (Wang et al., 2022) and BetterTogether framework (Soylu et al., 15 Jul 2024) decouple format or prompt learning (soft-prompt or discrete prompt tuning with frozen base) from full model tuning, thus preserving generalization while achieving task-specific adaptation.
• Graph-to-Text or Domain-Specialized Tasks: Intermediate staged fine-tuning on noisy, large-scale or domain-closely related data is applied before adaptation to smaller, high-quality target-task data. Tree-level or structural embeddings may be incorporated during stage one to stabilize learning and prevent hallucination (Wang et al., 2021).
• Architectural Adaptation and NAS: Two-stage methodologies may first search for an optimal architecture (architectural fine-tuning via RL-based NAS) and then transfer pretrained weights with selective parameter updating (Kim et al., 2022).

3. Technical Design Patterns and Loss Functions

Two-stage frameworks use diverse mechanisms for parameter selection, learning rate scheduling, and loss composition:

• Parameter Freezing/Unfreezing Schedules: Early stages often restrict updates to a subset (e.g., final classifier, attention filters, prompt modules), while later stages unfreeze the backbone or extend fine-tuning to deeper layers.
• Orthogonal Initialization and Regularization: To maximize the diversity of learned attention maps or adapters (as in Coarse2Fine), singular value decomposition (SVD) can be used for weight initialization, with optional regularization to maintain near-orthogonality across filters.
• Composite Loss Functions: Stage-specific losses may include attention center loss, class-balanced loss, or topological losses (e.g., based on persistent homology in SDF-TopoNet (Wu et al., 14 Mar 2025)) in tandem with standard cross-entropy or reconstruction objectives.

The following table summarizes typical architectural and procedural variants:

Domain/Task	Stage 1	Stage 2	Parameter Update
FGVC (Coarse2Fine)	Attention/localization	Fine classifier	Orthogonalized filters, joint loss
Distillation (LightPAFF)	Pretrain distillation	Task-specific distillation	Student model (all/frozen layers)
Imbalanced Classification	Final layer, reweight	Full model, standard loss	Sequential unfreezing
Prompt/Format Decoupling	Prompt only	Prompt+model	Soft prompt then weights

4. Performance Gains, Efficiency, and Generalization

Empirical results substantiate the effectiveness of two-stage fine-tuning:

• Classification Tasks: Top-1 accuracies are consistently improved. For example, on CUB-200-2011, Coarse2Fine achieves 89.5% vs. prior 89.4%; on iNaturalist 2017, 70.5% vs. 68.9%. Fine-grained face attribute recognition sees ~2% accuracy gains for specific features (Eshratifar et al., 2019).
• Distilled and Compact Models: Student models in LightPAFF reach 92.9% SST-2 accuracy with 25M parameters (vs. 93.5% BERT Large, 110M params), with 5x–7x inference speed-up (Song et al., 2020).
• Imbalanced Tasks: Two-stage fine-tuning demonstrates stark improvement for minority-class F1 scores and better out-of-distribution generalization when tested on shifted datasets; for example, improvements on SST-2 and ADME semantic labeling (ValizadehAslani et al., 2022).
• Memory/Compute Efficiency: By restricting trainable parameters in early stages (parameter-efficient tuning, orthogonal initialization, or prompt-only adaptation), two-stage protocols facilitate low-resource or rapid deployment scenarios, an essential property for edge learning or online inference (Song et al., 2020, Lyu et al., 1 Apr 2024).
• Generalization and Catastrophic Forgetting: Format specialization is mitigated in NLP by ProMoT's staged updating, preserving in-context generalization across task formats even after task-specific tuning (Wang et al., 2022). Replay or regularization in later stages helps to preserve previously mastered knowledge in knowledge-rich LLMs (Li et al., 8 Oct 2024).

5. Trade-Offs, Limitations, and Optimization

A two-stage fine-tuning approach introduces design trade-offs:

• Optimization Complexity: The additional stage may add hyperparameters (learning rate, replay ratio, constraint weight) and requires staged scheduling; however, this is offset by greater stability and convergence speed in many cases.
• Performance Ceiling: In some settings (e.g., re-ranking with cross-encoders), two-stage regimes do not outperform well-tuned single-stage contrastive models, highlighting that efficacy can be domain and loss-specific (Pezzuti et al., 28 Mar 2025).
• Parameter Efficiency vs. Expressiveness: Parameter-efficient tuning (e.g., soft prompts or adapters) targets minimal adaptation cost, but full expressiveness and task transfer may require broad unfreezing or auxiliary adapters for maximal downstream performance, especially in complex multimodal or generative tasks (Xia et al., 11 May 2025, Wu et al., 11 Sep 2024).
• Data Requirements: The choice of datasets for each stage (large, noisy or domain related in stage 1; fine-labeled, task-specific in stage 2) impacts model robustness and the risk of spurious correlations or "hallucination" in generative tasks (Wang et al., 2021).

6. Broader Applications and Research Directions

Two-stage fine-tuning has been extended to and inspired developments in multiple areas:

• Medical and Multilingual LLM Adaptation: Strategic two-stage instruction tuning allows for injection of domain-specific knowledge before task specialization, efficiently adapting LLMs for multilingual or low-resource applications in medicine and reasoning (Zhou et al., 9 Sep 2024, Zhang et al., 17 Dec 2024).
• Temporal and Topological Constraints: In video editing and segmentation, decoupled stages target temporal consistency and visual or topological detail separately via norm tuning, adapters, or dynamic threshold modules (Xia et al., 11 May 2025, Wu et al., 14 Mar 2025).
• Resource-Aware Edge Learning: In federated and edge settings, joint optimization of communication, computation, and batch size across pre-training and fine-tuning balances energy, latency, and accuracy (Lyu et al., 1 Apr 2024).
• Adaptation to Out-of-Distribution or Small-Scale Data: Fine-tuning data-driven estimators with perturbation sets centered on model-synthesized pseudo-observations mitigates OOD performance collapse in scientific and industrial digital twin scenarios (Lakshminarayanan et al., 6 Apr 2025).

Ongoing research involves refining replay mechanisms for knowledge retention, investigating alternative regularization for orthogonality or diversity in attention and adapter modules, and developing task-agnostic but highly specialization-capable architectures. New work also addresses modular approaches that combine discrete prompt and continuous weight optimization in alternating or even collaborative loops (Soylu et al., 15 Jul 2024).

7. Conclusion

The two-stage fine-tuning strategy embodies a principled approach to model adaptation, enabling hierarchical learning, greater robustness, and efficiency in a wide array of machine learning tasks. Rigorous experiments across domains demonstrate measurable gains in accuracy, generalization, and efficiency, provided the stages are appropriately specified and the empirical trade-offs carefully considered. Its ongoing evolution continues to influence transfer learning, supervised adaptation, parameter-efficient model deployment, and robust continual learning paradigms.