Enterprise AI Analysis
IN-RUN DATA SHAPLEY FOR ADAM OPTIMIZER
Reliable data attribution is essential for mitigating bias and reducing computational waste in modern machine learning, with the Shapley value serving as the theoretical gold standard. While recent "In-Run" methods bypass the prohibitive cost of retraining by estimating contributions dynamically, they heavily rely on the linear structure of Stochastic Gradient Descent (SGD) and fail to capture the complex dynamics of adaptive optimizers like Adam. In this work, we demonstrate that data attribution is inherently optimizer-dependent: we show that SGD-based proxies diverge significantly from true contributions under Adam (Pearson R ≈ 0.11), rendering them ineffective for modern training pipelines. To bridge this gap, we propose Adam-Aware In-Run Data Shapley. We derive a closed-form approximation that restores additivity by redefining utility under a fixed-state assumption and enable scalable computation via a novel Linearized Ghost Approximation. This technique linearizes the variance-dependent scaling term, allowing us to compute pairwise gradient dot-products without materializing per-sample gradients. Extensive experiments show that our method achieves near-perfect fidelity to ground-truth marginal contributions (R > 0.99) while retaining ~95% of standard training throughput. Furthermore, our Adam-aware attribution significantly outperforms SGD-based baselines in data attribution downstream tasks.
Revolutionizing Data Attribution in Adaptive AI Training
Our latest analysis uncovers a critical flaw in current data attribution methods for modern AI. We present a novel framework that not only addresses this gap but also sets a new standard for efficiency and accuracy in enterprise AI applications.
0.99
Near-Perfect Fidelity
95%
High Training Throughput
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Negligible Memory Overhead
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Our analysis identifies a critical gap in current data attribution for modern AI. Existing "In-Run" Shapley methods are tailored for Stochastic Gradient Descent (SGD), which operates on linear dynamics. However, the vast majority of deep learning models today are trained with adaptive optimizers like Adam, which introduce non-linear, stateful updates. We found that applying SGD-based attribution proxies to Adam-trained models leads to highly unreliable results, with a Pearson correlation coefficient of only R ≈ 0.11 to true marginal contributions.
This fundamental mismatch underscores that data value is not an intrinsic property of the dataset but is inherently coupled to the optimization dynamics. Without an optimizer-aware framework, enterprise AI deployments risk misinterpreting data influence, leading to suboptimal data curation, wasted computational resources, and potentially amplified biases.
To bridge the gap, we propose Adam-Aware In-Run Data Shapley, the first closed-form estimator specifically designed for Adam. Our approach redefines per-iteration utility under a fixed-state assumption and employs a first-order Taylor expansion to the adaptive variance term. This allows us to derive a tractable formula that explicitly accounts for both momentum and variance scaling inherent in Adam's updates.
A core innovation is the Linearized Ghost Approximation. This technique transforms the non-linear Adam update into a linear combination of the current gradient and historical moments. This linearization is critical for enabling scalable computation of pairwise gradient dot-products without the prohibitive memory cost of materializing per-sample gradients, a bottleneck in previous methods.
Our method achieves near-perfect fidelity (R > 0.99) to ground-truth marginal contributions under Adam dynamics, significantly outperforming SGD-based proxies (R ≈ 0.74).
In practical downstream tasks, Adam-aware attribution demonstrates superior performance:
- For data pruning on SST-2, our method is robust and consistently outperforms random pruning, yielding higher validation accuracy (up to 0.8876) by identifying and removing low-value samples. SGD-based pruning, in contrast, shows marked instability and performance degradation.
- In semantic source identification, our Adam-aware attribution significantly outperforms SGD-based baselines, consistently assigning low ranks to true source samples even under significant paraphrasing, proving its ability to capture optimizer-mediated semantic contribution beyond lexical overlap.
The Linearized Ghost Approximation ensures our method is highly scalable. It maintains approximately 95% of standard training throughput (87.85 samples/sec vs. 92.41 for standard AdamW), making it feasible for real-time data valuation without acting as a bottleneck.
Crucially, our technique introduces negligible memory overhead, with peak GPU memory usage virtually identical to standard AdamW training (5179.6 MB vs. 5179.0 MB). This is a stark contrast to naive implementations that require explicitly storing per-sample gradients and optimizer states, leading to a 150% increase in memory consumption (12965.0 MB) and severe bottlenecks for large-scale models.
This unprecedented efficiency makes Adam-aware In-Run Data Shapley practical for modern foundation model training, enabling effective data curation and robust source identification at scale.
R ≈ 0.11
SGD vs. Adam Fidelity
Traditional SGD-based data attribution proxies show extremely low correlation with true marginal contributions under Adam (Pearson R ≈ 0.11), rendering them ineffective for modern adaptive training pipelines. This highlights that data value is inherently optimizer-dependent, not an intrinsic property of the dataset.
| Adam-Aware In-Run Shapley | Traditional SGD-based Proxies |
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Our proposed Adam-Aware In-Run Data Shapley specifically addresses the challenges of adaptive optimizers. Unlike traditional SGD-based methods, it provides a closed-form estimator that restores additivity and explicitly accounts for Adam's momentum and variance scaling, achieving significantly higher fidelity to true marginal contributions.
Enterprise Process Flow: Linearized Ghost Approximation
Non-linear Adam Update
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First-order Taylor Expansion
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Linearized Ghost Approximation
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Pairwise Gradient Dot-Products
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Scalable Computation
The Linearized Ghost Approximation overcomes the non-linearity of Adam updates, enabling efficient computation. This technique approximates the Adam update as a linear combination of current gradients and historical moments, allowing all pairwise gradient dot-products to be computed in a single backpropagation pass, reducing memory overhead and maintaining high throughput.
0.8876
Max Validation Accuracy (Adam-aware pruning)
In data pruning tasks on SST-2, Adam-aware In-Run Shapley consistently outperforms random pruning and is significantly more stable than SGD-based pruning. Removing bottom-ranked samples according to our method yields validation accuracies up to 0.8876, proving its practical effectiveness in identifying uninformative or harmful data.
Enhanced Semantic Source Identification
Adam-aware attribution significantly outperforms SGD-based baselines in semantic source identification. It consistently assigns low ranks to true source samples even under significant paraphrasing and similar-topic perturbations, indicating that it captures optimizer-mediated semantic contribution rather than relying on surface-level lexical overlap.
Outcome: Superior robustness and accuracy in identifying influential data sources across varying semantic distances, crucial for maintaining data quality and preventing bias in large language models.
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Our Implementation Roadmap
A structured approach to integrate Adam-Aware Data Shapley into your existing AI workflows, ensuring a seamless transition and measurable impact.
01. Discovery & Assessment
In-depth analysis of your current AI training pipelines, data governance, and optimization strategies to identify key integration points for Adam-Aware Data Shapley.
02. Custom Integration
Our experts will tailor and integrate the Adam-Aware In-Run Shapley framework and Linearized Ghost Approximation into your specific deep learning environments and models.
03. Optimization & Deployment
Pilot deployment and fine-tuning to ensure optimal performance, fidelity, and efficiency. Comprehensive training for your team on leveraging attribution insights for data curation.
04. Monitoring & Scaling
Continuous monitoring of attribution performance, ongoing support, and strategic planning for scaling the solution across diverse AI applications within your enterprise.
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