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DTO-KD: Dynamic Trade-off Optimization for Effective Knowledge Distillation

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.7Download Report PDF
Knowledge Distillation
Abstract:

Knowledge Distillation (KD) is a widely adopted framework for compressing large models into compact student models by transferring knowledge from a high-capacity teacher. Despite its success, KD presents two persistent challenges: (1) the trade-off between optimizing for the primary task loss and mimicking the teacher's outputs, and (2) the gradient disparity arising from architectural and representational mismatches between teacher and student models. In this work, we propose Dynamic Trade-off Optimization for Knowledge Distillation (DTO-KD), a principled multi-objective optimization formulation of KD that dynamically balances task and distillation losses at the gradient level. Specifically, DTO-KD resolves two critical issues in gradient-based KD optimization: (i) gradient conflict, where task and distillation gradients are directionally misaligned, and (ii) gradient dominance, where one objective suppresses learning progress on the other. Our method adapts per-iteration trade-offs by leveraging gradient projection techniques to ensure balanced and constructive updates. We evaluate DTO-KD on large-scale benchmarks including ImageNet-1K for classification and COCO for object detection. Across both tasks, DTO-KD consistently outperforms prior KD methods, yielding state-of-the-art accuracy and improved convergence behavior. Furthermore, student models trained with DTO-KD exceed the performance of their non-distilled counterparts, demonstrating the efficacy of our multi-objective formulation for KD.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper proposes DTO-KD, a gradient-based framework that dynamically balances task loss and distillation loss in knowledge distillation through per-iteration multi-objective optimization. It resides in the 'Gradient-Based Multi-Objective Distillation' leaf, which contains only two papers including the original work. This leaf sits within the broader 'Knowledge Distillation with Multi-Objective Formulations' branch, indicating a relatively sparse research direction compared to more crowded areas like multi-teacher distillation or domain-specific applications. The taxonomy shows that gradient-level conflict resolution in distillation remains an emerging subfield with limited prior exploration.

The taxonomy reveals several neighboring research directions that contextualize this work. Adjacent leaves include 'Multi-Objective Adversarial Distillation' (combining robustness with distillation) and 'Multi-Objective Distillation for Real-Time Applications' (optimizing latency alongside accuracy). The broader 'Multi-Teacher Knowledge Distillation' branch addresses similar balancing challenges but through teacher selection rather than gradient manipulation. Meanwhile, 'Dynamic Multi-Objective Evolutionary Algorithms' tackle changing objectives using population-based search, offering a fundamentally different optimization paradigm. DTO-KD's gradient projection approach distinguishes it from these evolutionary methods while sharing conceptual ground with its single sibling paper in addressing gradient-level conflicts.

Among 27 candidates examined across three contributions, the gradient-level conflict resolution mechanism shows the most substantial prior work overlap. Specifically, 10 candidates were examined for this contribution, with 2 appearing to provide refutable prior work on gradient conflict and dominance handling. The dynamic trade-off optimization framework itself examined 10 candidates with no clear refutations, suggesting relative novelty in its specific formulation. The closed-form solution for loss weighting examined 7 candidates, also without refutations. These statistics indicate that while the core framework appears relatively novel within the limited search scope, the gradient manipulation techniques may have more established precedents in the examined literature.

Based on the top-27 semantic matches examined, the work appears to occupy a sparsely populated research direction with limited direct competition in its specific leaf. However, the analysis acknowledges that gradient-based multi-objective optimization is an active area, and the refutable findings for one contribution suggest that certain technical mechanisms may have been explored in related contexts. The taxonomy structure indicates that while the overall field of multi-objective distillation is well-developed, this particular gradient-projection approach represents a less-explored angle within that landscape.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
27
Contribution Candidate Papers Compared
2
Refutable Paper

Research Landscape Overview

Core task: knowledge distillation with dynamic multi-objective optimization. This field sits at the intersection of model compression and multi-objective decision-making, where practitioners seek to balance competing goals—such as accuracy, efficiency, and robustness—while transferring knowledge from large teacher networks to compact student models. The taxonomy reveals several major branches: Dynamic Multi-Objective Evolutionary Algorithms explore population-based search with changing objectives (Twin Population Transfer[3], Changing Objectives Transfer[6]); Knowledge Distillation with Multi-Objective Formulations addresses gradient-based trade-offs among distillation losses; Multi-Teacher Knowledge Distillation orchestrates ensembles of teachers (Multi-Teacher Dynamic Selection[8], AMMKD[11]); Self-Knowledge and Intra-Network Distillation refines representations within a single architecture (Self-Knowledge Distillation[12]); Model Compression with Multi-Objective Optimization jointly optimizes size and performance (Convex Quantization[2], Sparse Large-Scale[13]); Domain-Specific Applications adapt these techniques to specialized tasks (Hyperspectral Pruning[17], Alzheimer Multi-Modal[23]); Federated and Distributed Learning with Distillation handles privacy-preserving scenarios (Multimodal Federated Learning[30], Federated Defect Detection[33]); and Multi-Objective Optimization Foundations provide algorithmic underpinnings (Knowledge Gradient Multi-Objective[40]). A particularly active line of work focuses on gradient-based multi-objective distillation, where methods must navigate conflicting loss surfaces in real time. DTO-KD[0] exemplifies this direction by dynamically adjusting objective weights during training, closely aligning with Multi-Objective Divergence[25], which also tackles gradient conflicts among multiple distillation targets. In contrast, evolutionary approaches like Twin Population Transfer[3] maintain diverse solution sets across generations, offering broader exploration at the cost of computational overhead. Meanwhile, multi-teacher frameworks such as AMMKD[11] and Multi-Teacher Dynamic Selection[8] emphasize adaptive teacher weighting rather than explicit Pareto optimization, highlighting a trade-off between interpretability and flexibility. DTO-KD[0] occupies a niche within gradient-based formulations, sharing conceptual ground with Multi-Objective Divergence[25] in its emphasis on balancing distillation objectives through dynamic weighting, yet differing in how it adapts those weights over the course of training.

Claimed Contributions

DTO-KD: Dynamic Trade-off Optimization Framework for Knowledge Distillation

The authors introduce DTO-KD, a multi-objective optimization framework that formulates knowledge distillation as a gradient-level optimization problem. This framework dynamically balances task-specific and distillation objectives during training without requiring manual hyperparameter tuning for loss weighting.

10 retrieved papers
Gradient-level Resolution of Conflict and Dominance via Per-iteration Balancing

The method addresses two critical gradient-based issues in knowledge distillation: gradient conflict (when task and distillation gradients are misaligned) and gradient dominance (when one objective suppresses the other). DTO-KD uses per-iteration gradient projection techniques to ensure balanced and constructive updates.

10 retrieved papers
Can Refute
Closed-form Solution for Dynamic Loss Weighting in Teacher-Student Architectures

The authors derive a closed-form analytical solution for computing optimal weights between distillation and task losses at each training step. This solution produces update directions jointly aligned with both objectives and can be computed efficiently, unlike general multi-objective methods that require iterative optimization.

7 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

DTO-KD: Dynamic Trade-off Optimization Framework for Knowledge Distillation

The authors introduce DTO-KD, a multi-objective optimization framework that formulates knowledge distillation as a gradient-level optimization problem. This framework dynamically balances task-specific and distillation objectives during training without requiring manual hyperparameter tuning for loss weighting.

Contribution

Gradient-level Resolution of Conflict and Dominance via Per-iteration Balancing

The method addresses two critical gradient-based issues in knowledge distillation: gradient conflict (when task and distillation gradients are misaligned) and gradient dominance (when one objective suppresses the other). DTO-KD uses per-iteration gradient projection techniques to ensure balanced and constructive updates.

Contribution

Closed-form Solution for Dynamic Loss Weighting in Teacher-Student Architectures

The authors derive a closed-form analytical solution for computing optimal weights between distillation and task losses at each training step. This solution produces update directions jointly aligned with both objectives and can be computed efficiently, unlike general multi-objective methods that require iterative optimization.

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