Proficiency-Aware Multitask Learning

Updated 19 October 2025

The paper introduces a dynamic framework that estimates task proficiency to tailor and optimize inter-task knowledge sharing.
It employs architectural innovations like task embeddings, parameter routing, and attention mechanisms to mitigate negative transfer.
Empirical results demonstrate improved convergence and efficiency across domains such as NLP, computer vision, and speech recognition.

Proficiency-aware multitask learning refers to frameworks and methodologies that enable a learning system to both share information across multiple tasks and adaptively regulate such sharing based on the suitability or proficiency of the transfer. Unlike conventional multitask systems that statically define knowledge sharing, proficiency-aware approaches seek to estimate or anticipate the relative benefit of different sharing patterns and dynamically adjust model selection, parameter allocation, optimization schedules, or inductive regularization according to task characteristics and empirical historical outcomes. Such methods span domains including deep neural networks, natural language processing, computer vision, multi-modal modeling, and speech recognition, addressing classic challenges like negative transfer, imbalance, and domain adaptation with both architectural and optimization-level innovations.

1. Defining Proficiency Awareness in Multitask Learning

Proficiency awareness in multitask learning denotes a system's ability to tailor inter-task knowledge transfer based on the measured or anticipated proficiency of potential sharing patterns. This encompasses mechanisms that (i) represent intrinsic task differences via learned meta-features or embeddings, (ii) estimate the impact of joint modeling compared to single-task learning using historical performance, and (iii) optimize the sharing, separation, or adaptation of model parameters in light of these estimates.

A reference implementation is the L2MT framework (Zhang et al., 2018), which formalizes proficiency via the relative test error $o = \varepsilon_\text{MTL} / \varepsilon_\text{STL}$ and learns an estimation function $f(E, \Omega)$ to predict the transformed error for a candidate multitask model, using the task embedding matrix $E$ and covariance structure $\Omega$ . Proficiency awareness is enacted by minimizing $f(E, \Omega)$ for new multitask problems, thus selecting or learning the model best suited to their structure.

2. Architectural and Algorithmic Foundations

Proficiency-aware multitask models rely on both architectural and algorithmic innovations:

Task Embeddings and Meta-Representations: Systems such as L2MT generate task embeddings using a layerwise graph neural network (LGNN), summarizing both feature structure and relational information (Eq: $H_i = \mathrm{ReLU}(L_i^\top X + H_{i-1} G + \beta_i 1^\top)$ ), producing meta-representations that guide model selection (Zhang et al., 2018).
Parameter Allocation and Routing: Adaptive frameworks employ binary allocation matrices learned via Gumbel-Softmax (Maziarz et al., 2019), allowing fine-grained dynamic grouping of components across layers. When extended to proficiency awareness, allocation probabilities can be modulated by task-specific proficiency features.
Auxiliary Modules and Adaptive Regularization: Auxiliary learning modules, designed via neural architecture search or attention-based adaptation, regularize shared representations during training by both directly injecting gradient signals and introducing inductive bias (Liu et al., 2019). These modules can potentially be adapted in strength or configuration according to task proficiency.
Attention and Distillation Mechanisms: Architectures like ATI-Net (Sinodinos et al., 2022) integrate knowledge distillation at the latent feature level, employing self-attention to dynamically gate distilled information. This enables selective enhancement of less proficient tasks by stronger auxiliary tasks, closely aligning with the goals of proficiency-aware sharing.
Optimization Schedulers and Task Grouping: Novel meta-optimizers measure gradient interference and group tasks according to compatibility, for example using greedy graph coloring over interference graphs (Patapati et al., 21 Sep 2025) or sequential group updates based on proximal inter-task affinity (Jeong et al., 17 Feb 2025). This prevents negative transfer by updating only non-conflicting groups, enhancing task-specific adaptation.

3. Estimation and Measurement of Proficiency

Performance estimation in proficiency-aware frameworks is central. In L2MT, the transformed relative error is the target, and the estimation function includes terms like $\operatorname{tr}(E^\top E \Omega)$ and $\operatorname{tr}(K \Omega)$ , capturing both linear and nonlinear kernel consistency. Symbolic regression and simulation methods can empirically quantify task relatedness and data sufficiency, capturing scalability laws such as $F_1 \propto \sqrt{n}$ (number of samples), $F_1 \propto \sqrt{T}$ (number of tasks), and $F_1 \propto \sqrt{\mathrm{AMI}}$ (adjusted mutual information/task relatedness) (Bettgenhäuser et al., 2020).

Block-sparse regularization and robust feature selection in multi-task prediction settings (e.g., zero-shot performance in multilingual models (Ahuja et al., 2022)) further enable identification of universal features that drive proficiency across a suite of tasks.

Mitigating negative transfer is a central objective. Classical multitask models, which aggregate losses with fixed or adaptive weights, can suffer from coarse granularity and fail to suppress task-level interference. Proficiency-aware methods enhance granularity via:

Class-Wise Arbitration: Per-class weighting mechanisms dynamically learn whether an auxiliary class contributes positively or negatively to the main task, updating weights based on the empirical effect on main task loss (Eqs. 2–7) (Yim et al., 2020).
Gradient-Based Task Selection: Interference-aware grouping activates only tasks with aligned gradients, thus preserving descent direction and reducing destructive updates (Patapati et al., 21 Sep 2025).
Uncertainty-Weighted Training: Impartial Auxiliary Learning estimates task-dependent uncertainties (Eq. $L_\text{MTL} = \sum_t \frac{1}{2\sigma_t^2} L_t + \log \sigma_t$ ), adjusting auxiliary influence both in decoder and encoder layers to ensure robust optimization, especially when auxiliary pseudo-labels are noisy (Li et al., 27 Dec 2024).

5. Empirical Performance, Robustness, and Scalability

Proficiency-aware multitask learning demonstrates marked empirical benefits:

Superior Multi-Task Gains: L2MT achieved lower relative test errors than baseline multitask learners, adapting knowledge sharing through automatic model selection (Zhang et al., 2018).
Resilience to Data Imbalance: Meta-learning approaches with adaptive sampling (temperature-based, parameterized) balance high- and low-resource tasks/languages, yielding improved zero-shot generalization (Tarunesh et al., 2021).
Fine-Grained Regularization: Auxiliary modules and task-adaptive low-rank representations (as in TA-LoRA (Zhang et al., 20 Apr 2025) and MTL-LoRA (Yang et al., 12 Oct 2024)) provide parameter efficient means to specialize tasks, perform domain adaptation, and suppress interference, without increasing trainable parameters significantly.
Dynamic Task Grouping and Optimization Efficiency: Sequential or scheduler-based group updates reduce memory and time cost, scale to larger task sets, and improve convergence and overall multi-task metric $\Delta_m$ (Jeong et al., 17 Feb 2025, Patapati et al., 21 Sep 2025).

6. Applications and Extension to Diverse Domains

Proficiency-aware multitask strategies have proliferated across domains:

Computer Vision and Scene Understanding: Cross-Task Affinity Learning (CTAL) captures both local and long-range dependencies, refining dense scene predictions by diffusing affinity matrices over grouped convolutions (Sinodinos et al., 20 Jan 2024). Edge-device applications benefit from parameter efficiency and targeted prediction refinement.
NLP and Multilingual Processing: Meta-learned models adapt to unseen tasks and languages, adjusting learning strategies according to task abstraction and proficiency, evidenced by consistent improvements over multi-task and language-centric baselines (Tarunesh et al., 2021, Ahuja et al., 2022).
Speech Recognition: Proficiency-aware multitask ASR (Sun et al., 12 Oct 2025) integrates auxiliary proficiency classifiers, leveraging additional supervision to both lower word error rates and narrow proficiency gaps in learner populations, especially when data distribution is imbalanced.
Industrial and Real Domain Datasets: MTL-LoRA demonstrates robust adaptation for industrial text Ads relevance, NLU, and image-text understanding by isolating low-rank updates per task, showing superior generalization and efficiency (Yang et al., 12 Oct 2024).

7. Theoretical Guarantees and Future Directions

Theoretical analyses reinforce the proficiency-aware paradigm:

Gradient Alignment and Convergence Guarantees: Grouping tasks with high affinity aligns gradients, with formal bounds demonstrating improved convergence rates and decreased joint multi-task loss (Jeong et al., 17 Feb 2025, Patapati et al., 21 Sep 2025).
Symbolic and Empirical Performance Laws: The empirical laws $F_1 \propto \sqrt{n},\ F_1 \propto \sqrt{\mathrm{AMI}},\ F_1 \propto \sqrt{T}$ conceptualize proficiency gain as a function of data, relatedness, and joint optimization (Bettgenhäuser et al., 2020).

Looking forward, further research may explore meta-learning-driven proficiency estimation, dynamic reallocation of adaptation budgets (e.g., rank in low-rank modules), architecture tuning based on evolving proficiency signals, and hybrid optimization algorithms that combine interference-aware scheduling with uncertainty-guided regularization to further ameliorate negative transfer and scale adaptation to large heterogeneous task sets.

Proficiency-aware multitask learning is thus characterized by its measurement-driven, dynamically adaptive sharing and optimization strategies, enabling robust, efficient, and equitable model performance across diverse and challenging multi-domain settings.