Multi-phase Fine-Tuning Strategies

• Multi-phase fine-tuning is a technique that adapts pre-trained models through sequential or parallel stages to enhance robustness and generalization.
• It leverages phased data scheduling, adaptive interventions, and specialized parameter isolation to mitigate overfitting and catastrophic forgetting.
• Empirical studies demonstrate significant gains in language modeling, low-resource adaptation, and multi-task optimization over single-stage methods.

Multi-phase fine-tuning is a family of strategies that structure the adaptation of a pre-trained model into multiple sequential or parallel phases rather than performing all supervised adaptation in a single stage. These strategies introduce various mechanisms—such as staged data scheduling, orthogonal parameterizations, adaptive or task-specific interventions, or iterative specialization—intended to improve robustness, generalization, stability, or efficiency of fine-tuned models. Multi-phase fine-tuning has demonstrated empirical advantages over traditional one-stage procedures in a variety of domains, notably LLM robustness, low-resource adaptation, curriculum-style instruction alignment, class-imbalanced classification, continual learning, and multi-task optimization.

1. Conceptual Framework and Core Principles

Multi-phase fine-tuning formally refers to any fine-tuning process in which adaptation to a supervised task is carried out through multiple, architecturally or procedurally distinct stages. This can be instantiated in a number of ways, including:

• Sequential training stages, each with different data distributions, loss functions, or targeted parameter subsets (e.g., (Xu et al., 2021, ValizadehAslani et al., 2022, Pang et al., 1 Jun 2024, Li et al., 8 Oct 2024)).
• Parallel or multiverse structures that allow multiple classifier heads or adaptation modules to be trained concurrently with explicit constraints or adaptive selection (Malkiel et al., 2019, Shen et al., 2022).
• Specialized parameter isolation and fusion phases that mitigate destructive interference or catastrophic forgetting between tasks (Wang et al., 29 Aug 2025, Aggarwal et al., 21 Oct 2024).
• Task or example curriculum scheduling, so model complexity is increased in step with input difficulty (Pang et al., 1 Jun 2024, Li et al., 8 Oct 2024).
• Alternating training regimes using different algorithms for different task types (e.g., SFT for intuitive responses, RL for multi-step reasoning (Huang et al., 28 Jul 2025)).

This approach is motivated by understanding that (1) different data complexities, domains, or tasks place incompatible demands on model parameters, (2) abrupt domain or task shifts can cause overfitting or forgetting, and (3) single-stage procedures lack adaptive flexibility to exploit rich data or task structure.

2. Multi-phase Fine-Tuning Architectures and Methodologies

Several concrete architectures and workflows appear in contemporary literature:

Method/Strategy	Main Principle	Example Citation
Parallel classifier heads + adaptive orthogonality	Robustness via diverse classifier ensemble and head pruning	(Malkiel et al., 2019)
Gradual fine-tuning (curriculum domain shift)	Sequential data mixing from out-of-domain to in-domain	(Xu et al., 2021)
Phase-wise instruction alignment	Instruction difficulty ordering and phased uptraining	(Pang et al., 1 Jun 2024)
Core parameter isolation and fusion	Per-task parameter selection, grouping, and SLERP fusion	(Wang et al., 29 Aug 2025)
Two-stage reweighting for class-imbalance	Head-only class-reweighted loss, then all-parameter FT	(ValizadehAslani et al., 2022)
Continual fine-tuning with replay/layer freezing	Task-similarity-aware sequential FT with targeted mitigation	(Aggarwal et al., 21 Oct 2024)
Multi-phase curriculum via knowledge type	“Maybe known”→expanded via reclassification+replay	(Li et al., 8 Oct 2024)
Progressive staged adaptation for translation	(CPT+ITTL): domain+auxiliary pre-FT, then task-specific FT	(Thillainathan et al., 28 Mar 2025)
Dual-system (SFT then RL) LoRA partitioning	System 1/2 parameter specialization per task type	(Huang et al., 28 Jul 2025)

In all cases, the methodology introduces at least one discrete transition between adaptation phases with different optimization targets, constraints, or learning signals.

3. Theoretical Rationale and Empirical Outcomes

Curriculum and Progressive Learning

Phased approaches grounded in curriculum or progressive alignment hypotheses suggest that model alignment to task or instruction structure is a gradual process. Phased IFT (Pang et al., 1 Jun 2024) demonstrates that ordering training by GPT-4-assessed instruction difficulty and proceeding from easy to hard enhances instruction-following performance: win rates improve by +7.26% on average, and permuting the curriculum order to place hard items first diminishes gains or leads to negative transfer.

Model Robustness and Overfitting

Parallel-head or multiverse approaches with orthogonality constraints enforce diversity among classifiers and integrate adaptive pruning via clustering of head performance (Malkiel et al., 2019). On the GLUE benchmark, this yields up to +9% accuracy improvement in cross-dataset settings over standard BERT fine-tuning, with reduced susceptibility to domain shift and overfitting on small data.

Catastrophic Forgetting and Knowledge Retention

Gradual or staged procedures are especially effective at mitigating task interference. Parameter isolation and freezing core task-specific regions (Wang et al., 29 Aug 2025) protect against the destructive seesaw phenomenon, and evaluation demonstrates consistent outperformance of naive multi-task or random multi-stage fine-tuning across diverse reasoning and code-generation tasks.

Efficiency and Scalability

Parameter-efficient approaches (e.g., LoRA-PAR (Huang et al., 28 Jul 2025), CGC-LoRA (Song et al., 22 Jan 2024), prompt-based PEFT (Peng et al., 5 Sep 2025)) explicitly scope parameter updates to targeted subregions or modules. Staged training on fast/intuitive and deliberate/logical tasks, using SFT followed by RL on split parameter subspaces, enables comparable performance to full PEFT baselines with substantial reductions in active parameter count.

4. Representative Algorithms and Formulations

Several technical formulations underpin these strategies:

Parallel-head Multiverse Loss (Malkiel et al., 2019)

Let $C_j(d_i) = d_i^\top F^{(j)} + b_j$ be the $j$th classifier head over latent $d_i$.

• Orthogonality enforced via multiverse loss:

$$\mathcal{L}_{mv} = \sum_{j, r, s>r} |(f_j^r)^\top f_j^s \cdot \beta_r \cdot \beta_s|$$

• Heads are pruned via clustering on moving average task loss.

Gradual Fine-Tuning Algorithm (Xu et al., 2021)

Stage-wise data schedule $$S = \{4K \rightarrow 2K \rightarrow 0.5K \rightarrow 0\}$$:

\For{amount in S} D_t \gets Sample(D_{t-1}, amount) D_t \gets D \cup D_t M_t \gets Train(M_{t-1}, D_t) \EndFor

Empirically yields +3.6% slot accuracy and +15.1% joint accuracy gains.

Phased Instruction Fine-Tuning (Pang et al., 1 Jun 2024)

• Each instruction $i$ receives a GPT-4 difficulty score $d_i$.
• Data is partitioned via chosen thresholds, e.g., $[1.0, 1.5), [1.5, 3.5), [3.5, 5]$.
• Model is uptrained sequentially, each phase increasing in instruction difficulty, with only outputs part of the loss unmasked.

Core Parameter Identification and SLERP Fusion (Wang et al., 29 Aug 2025)

• For task $T_i$, probe FT yields $$\Delta|\theta_j^{(i)}| = |\theta_j^{(i)} - \theta_j^{(0)}|$$.
• Core region $C_i$ defined by top $p$\% of parameters per update magnitude.
• Merged backbone assigns $\theta_{fused,j} = \theta_j^{(i)}$ if $j\in C_i$, otherwise blends via SLERP between individual and base values.

5. Domain-Specific Applications and Empirical Impact

Low-Resource and Domain Adaptation

Multi-phase FT is especially important in low-resource domain adaptation (e.g., machine translation (Thillainathan et al., 28 Mar 2025), dialogue state tracking (Xu et al., 2021)). The continual pre-training (CPT) phase on in-domain monolingual data followed by intermediate task transfer learning (ITTL) with both in-domain and out-domain parallel corpora yields BLEU improvements averaging +1.47 over single-stage baselines, with ensemble gains above +2 BLEU. Gradual FT or CPT strategies enable models to leverage scarce in-domain data via staged alignment.

Class Imbalance

Two-stage FT for long-tailed classification (ValizadehAslani et al., 2022) demonstrates that initially adapting only the classification head with a class-reweighted loss (e.g., LDAM) and then shifting to whole-model FT preserves minority class performance. Micro F1 improvements up to 0.9116 (vs. 0.9021 for vanilla FT) are observed on ADME semantic labeling, with pronounced per-class gains for rare classes.

Continual and Multi-Lingual Adaptation

Continual FT studies (Aggarwal et al., 21 Oct 2024) reveal that phase-wise dataset similarity is critical for retaining “task ability.” Dissimilar datasets induce representational drift and catastrophic forgetting, remediable by generative replay (injecting Phase 1 English responses into Phase 2 multilingual training) or targeted layer freezing; task and language abilities are thus jointly preserved.

Multi-task Instruction Tuning

Multi-task instruction tuning with prompt-based PEFT and LoRA (Peng et al., 5 Sep 2025) demonstrates that sequential, multi-dataset training using mixed instructional templates achieves up to +37% F1 zero-shot improvement for patient information extraction, while maintaining high few-shot performance.

6. Limitations, Challenges, and Future Directions

Although multi-phase fine-tuning consistently outperforms single-stage, vanilla approaches in terms of robustness, generalization, and adaptation efficiency, several limitations are documented:

• Hyperparameter sensitivity, notably to the timing, size, and weighting of different phases (Song et al., 22 Jan 2024, Huang et al., 28 Jul 2025).
• Added complexity from phase transitions, additional loss terms, and architecturally modular designs.
• Occasional performance regressions (e.g., CoLA in (Malkiel et al., 2019)), often requiring further tuning or dataset-specific adjustment.
• Scalability of task grouping (e.g., for parameter isolation) to very large task sets or highly heterogeneous objectives.
• Generalization to continual or lifelong learning regimes with evolving or expanding task sets remains an open question.

Opportunities for further research include deeper automation of curriculum or parameter scheduling, architectural innovations for scalable task separation, and systematic paper on the interplay between multi-phase fine-tuning and privacy/robustness concerns.

7. Summary Table of Key Multi-phase Fine-Tuning Strategies

Approach	Key Mechanism	Empirical Advantage
Parallel/multiverse heads	Orthogonal/pruned classifiers	Robustness to domain shift; +9% acc. (Malkiel et al., 2019)
Gradual FT (domain schedule)	Stagewise data mixing	+3.6% slot/+15% joint accuracy (Xu et al., 2021)
Two-stage (class imbalance)	Reweighted head, then full FT	Minority F1 boost; generalization gains (ValizadehAslani et al., 2022)
Instruction phased curriculum	Difficulty-stratified IFT	+7% avg. win rate; progressive alignment (Pang et al., 1 Jun 2024)
Core parameter isolation	Probe FT; SLERP fusion	Reduced interference/forgetting (Wang et al., 29 Aug 2025)
Continual (CFT w/ replay/freeze)	Similarity-aware mitigation	Preserved TA/LA; task drift resistance (Aggarwal et al., 21 Oct 2024)
LoRA-PAR dual system	SFT then RL partitioned LoRA	Task-relevant PEFT with reduced parameter count (Huang et al., 28 Jul 2025)
Multi-task instruction tuning	LoRA+prompt PEFT, staged	up to +37% zero-shot F1, high few-shot (Peng et al., 5 Sep 2025)

In conclusion, multi-phase fine-tuning represents a diverse design space united by the principle of staged or modular adaptation. Across domains, evidence shows that such strategies enable more robust and adaptable models with improved sample efficiency, retention, and transfer, especially under challenging data regimes or complex task portfolios.