Adaptive Task Weighting in Multi-Task Learning

• Adaptive task weighting in multi-task learning is a technique that dynamically adjusts each task's loss contribution to optimize joint performance and reduce negative transfer.
• It leverages methods like uncertainty-based scaling, gradient projection, and meta-learning to account for task difficulty, label noise, and varying learning rates.
• Empirical studies show that adaptive weighting improves accuracy and efficiency, with significant gains on benchmarks such as Cityscapes, CIFAR-100, and noisy auxiliary settings.

Adaptive task weighting in multi-task learning (MTL) refers to the principled real-time adjustment of loss or gradient contributions from multiple tasks to optimize joint or target-task performance, mitigate negative transfer, and enhance efficiency. The field encompasses a spectrum of techniques, from uncertainty-driven regularization to meta-learning, sample-level reweighting, gradient-projection, and data-driven empirical adaptation, with substantial empirical and theoretical foundations.

1. Foundations and Motivation for Adaptive Task Weighting

Multi-task learning typically involves optimizing a composite loss: $$\mathcal{L}_{\text{total}} = \sum_{t=1}^{T} w_t \mathcal{L}_t$$ where loss terms $\mathcal{L}_t$ for each task $t$ are scaled by weights $w_t$ and combined to train a shared model. The necessity for adaptive—rather than static—weighting arises from several phenomena:

• Diverse Task Difficulty and Dynamics: Individual tasks can have inherently different levels of difficulty or learning rates, requiring task-specific emphasis that changes over training time (Huq et al., 2023).
• Potential for Negative Transfer: Auxiliary tasks may be harmful if their signal does not align with the main task, or if label noise is present. Naïve weighting can exacerbate negative transfer (Yim et al., 2020, Kourdis et al., 13 May 2024).
• Cross-Task Interference and Gradient Conflict: Task losses may induce conflicting gradient signals, in which case uniform or static weightings lead to suboptimal representation learning (Bohn et al., 3 Sep 2024).
• Non-stationary Value of Auxiliary Tasks: The contribution of auxiliary tasks can vary as the shared representation evolves (Verboven et al., 2020, Kourdis et al., 13 May 2024).

Therefore, adaptive task weighting frameworks have been developed to dynamically allocate model capacity across tasks using meta-learning, uncertainty modeling, gradient-based criteria, or data-driven performance trends.

2. Classical and Uncertainty-based Weighting Approaches

Early adaptive MTL methods include:

• Loss-based Normalization: Weights proportional to current losses, e.g. $W_i = n\,\frac{L_i}{\sum_{j=1}^n L_j}$, where high-loss tasks are emphasized. This method is parameter-free and computationally minimal, but does not distinguish learned difficulty from noise or label errors (Huq et al., 2023).
• Uncertainty-based Weighting: Homoscedastic uncertainty is modeled as a per-task parameter $\sigma_t$; the composite loss takes the form

$$\mathcal{L}_{\mathrm{MTL}} = \sum_t \frac{1}{2\sigma_t^2}\mathcal{L}_t + \log \sigma_t$$

$\sigma_t$ is learned jointly with network parameters. Tasks with high predictive uncertainty are automatically down-weighted. This approach is foundational for many later methods and forms the backbone of recent state-of-the-art frameworks (Li et al., 27 Dec 2024, Tian et al., 2022, Boiarov et al., 2021).

• Group-Level and Hierarchical Uncertainty Weighting: Extensions cluster tasks by convergence rate and assign learnable group-level uncertainty parameters, achieving scalability and stability for models with many tasks (Tian et al., 2022).
• Limitations: Pure loss-based and uncertainty-based weighting can be non-robust in the presence of noisy tasks. Systems adapting solely to observed task loss can over-emphasize tasks with irreducible error due to label noise, leading to failure modes in jointly trained models (He et al., 3 Feb 2024).

3. Meta-Learning and Optimization-Driven Schemes

Meta-learning addresses adaptive weighting as a hyperparameter optimization or bi-level optimization problem. Techniques include:

• Simultaneous Perturbation Stochastic Approximation (SPSA): Weight hyperparameters are optimized using stochastic approximations based on random perturbations and loss differences, suitable for noisy, non-smooth objectives especially in meta-learning and low-data regimes (Boiarov et al., 2021).
• Meta-Learned Weighting via Validation Performance: Bi-level frameworks such as $\alpha$VIL adapt the task weights by minimizing validation loss on a target task, using meta-optimization over parameter deltas derived from single-task updates (Kourdis et al., 13 May 2024). This directly aligns weighting with final deployment objectives and enables robust detection of positive and negative transfer.
• Excess Risk-based Weighting: Weights are adjusted in proportion to each task's distance to its Bayes-optimal risk (i.e., convergence gap), isolating tractable error from irreducible noise (He et al., 3 Feb 2024). The approach maintains consistent performance under label noise due to this focus on excess risk rather than raw loss.
• Evolutionary Meta-Learning: Tasks' weights are optimized on validation sets via population-based search (e.g., Evolutionary Strategies), potentially with asynchronous task update schedules to balance learning speeds (Leang et al., 2020).

4. Gradient-Oriented and Sample-Local Weighting

Several methods adjust weights dynamically by examining gradient directions and alignment, moving beyond per-task loss levels:

• Gradient Norm and Gradient Projection Approaches: Methods such as GradNorm [cited in multiple sources] set weights so tasks' gradient contributions to the shared representation are balanced according to desired update rates. wPCGrad introduces probability-based task selection in projected gradient updates, where a task selected based on past loss statistics is left unmodified, and conflicting gradients from other tasks are projected onto its normal plane (Bohn et al., 3 Sep 2024).
• Sample-Level Weighting: SLGrad computes per-sample, per-task weights based on the dot product between each sample's gradient and the main task's validation gradient. Only samples contributing positively to main task generalization are retained, and harmful (negatively aligned) samples are downweighted to zero (Grégoire et al., 2023). This approach provides higher granularity for eliminating negative transfer.
• Hybrid Approaches: Recent algorithms combine uncertainty weighting in the decoder (i.e., per-task loss heads) with gradient norm balancing and uncertainty mapping in the shared encoder, as in the Impartial Auxiliary Learning method (IAL) (Li et al., 27 Dec 2024). This strategy is demonstrated to yield robust gains in both clean and noisy auxiliary settings.

5. Performance-Driven and Data-Empirical Weighting

Adaptive task weighting can be achieved without gradient information or explicit meta-objectives by leveraging empirical measures of per-task performance:

• Dynamic Accuracy-Based Adjustment: Methods such as DeepChest initialize weights according to single-task difficulty (via accuracy) and update during MTL epochs: tasks underperforming the average receive multiplicative weight increases while others are decayed (Mohamed et al., 29 May 2025). This requires only scalar per-task statistics per epoch and reduces both memory and computational overhead relative to gradient-based methods.
• Exponentiated Data Proportion Weighting: In settings with heterogeneous label availability, the weighting can be made a learnable function of the per-task sample fraction, with an exponent parameter $\beta_t$ adapted during training to optimize task trade-offs (Zhang et al., 4 Sep 2025).

6. Fine-Grained and Instance-Level Weighting

Recent frameworks adapt weights at even finer granularity:

• Instance-Level Task Parameters: A matrix of per-sample, per-task non-negative weights, learned via uncertainty-based regularization, allows selective up/down-weighting at the level of individual examples (Vasu et al., 2021). This architecture provides robustness to label noise and enables post hoc identification of corrupted labels through analysis of learned instance variances.
• Class-Wise Auxiliary Weighting: For auxiliary tasks with component structure (e.g., semantic segmentation), class-wise scaling factors are learned via feedback from the impact on main-task loss, suppressing negative transfer at the semantic subtask level (Yim et al., 2020).

7. Empirical Results and Comparative Analysis

Empirical evaluations consistently demonstrate the superiority of adaptive weighting over static or hand-tuned approaches. Significant findings include:

Method/Domain	Key Performance Metrics and Gains
IAL (Li et al., 27 Dec 2024)	ΔMTL up to +8.22% on Cityscapes; robust to noisy auxiliaries
AWA (Huq et al., 2023)	+1–3% accuracy gain on CIFAR-100/AGNews vs uniform, uncertainty, DWA, GradNorm
SLGrad (Grégoire et al., 2023)	2×–3× lower error in noisy auxiliary settings; maintains low main-task loss under heavy noise
ExcessMTL (He et al., 3 Feb 2024)	Retains near optimal clean-task accuracy with up to 80% label noise; outperforms all prior loss-based schemes
DeepChest (Mohamed et al., 29 May 2025)	+7% overall accuracy on chest X-ray multi-label; 3× speedup over PCGrad
Class-wise weighting (Yim et al., 2020)	Yields strictly lower main-task losses than best Pareto trade-off, by suppressing harmful classes in auxiliary task
Instance-level weighting (Vasu et al., 2021)	Maintains performance under 20–40% label corruption; up to 60% error reduction (SURREAL)
QW-MTL (Zhang et al., 4 Sep 2025)	Significant AUROC gains on ADMET endpoints with large task size heterogeneity
αVIL (Kourdis et al., 13 May 2024)	Matches or outperforms both standard MTL and DIW on MultiMNIST/GLUE; recovers from negative transfer

A plausible implication is that modern adaptive task weighting, especially when integrating uncertainty, meta-objective optimization, and gradient conflict resolution, is essential for state-of-the-art performance, noise robustness, and practical scalability in complex multitask systems.

8. Limitations, Controversies, and Future Directions

• Scaling and Overfitting: Some approaches (e.g., instance-level) incur $O(NT)$ memory for large $N,T$. Regularization and subsampling are practical mitigations (Vasu et al., 2021).
• Computational Overhead: Meta-learning-based and gradient-based strategies often double or triple training time, though performance-driven approaches such as DeepChest can offer significant speed-ups (Mohamed et al., 29 May 2025).
• Robustness to Label Noise: Loss- and uncertainty-based approaches can fail in noisy regimes, placing emphasis on excess risk or sample-level screening to avoid overweighting pathological tasks (He et al., 3 Feb 2024, Grégoire et al., 2023).
• Task Grouping and Dynamic Granularity: Most methods only adapt at the task or group level; integrating finer granularity (sample or class) weighting remains a direction of active development (Yim et al., 2020, Tian et al., 2022).
• Meta-Objective Design: Alignment between training-time weighting and deployment objectives is critical. Direct meta-optimization on target-validation or deployment metrics (as in $\alpha$VIL) appears particularly promising (Kourdis et al., 13 May 2024).

Further research is likely to explore the integration of meta-learning, sample-level adaptation, and resource-efficient empirical updating, robustifying adaptive weighting in large-scale, noisy, or distributionally complex MTL regimes.