Research·arXiv cs.CL·12h ago

New method measures training data influence without task selection

Researchers have developed a task-agnostic method for attributing model behavior to specific training examples, sidestepping the need to select arbitrary downstream tasks or validation sets. The approach measures influence by tracking how each example's gradient updates move parameters toward a model's final state, then reconstructs this signal from intermediate checkpoints without retraining. Testing across Pythia and PolyPythia configurations reveals systematic temporal patterns in data influence. This work matters because training data attribution remains a bottleneck for interpretability and model debugging. Practitioners can now reason about which pretraining examples shaped model capabilities without committing to task-specific evaluation schemes, opening paths for more principled data curation and contamination detection.

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Explainer

The key novelty is reconstructing influence signals from intermediate checkpoints without retraining. Prior work required either task-specific validation sets or full recomputation; this method extracts the same signal retroactively, making attribution tractable at scale.

This connects directly to the DFM Mimir work from August, which demonstrated that curated, permissible datasets can match frontier performance. Attribution methods like this one become essential infrastructure for that curation process: teams building models on restricted data need to verify which examples actually drove capability gains, not just assume diversity equals quality. The SAEVerbalizer paper from the same day also shares the underlying goal (interpretability without expensive external loops), though it operates at the feature level rather than the training example level. Together, these three papers reflect a maturing focus on making model internals and training decisions auditable without proportional cost increases.

If the Pythia and PolyPythia influence patterns hold up when applied to a contamination detection task (e.g., identifying which pretraining examples overlap with a held-out benchmark), that validates the method's practical utility. If instead the temporal patterns don't correlate with downstream task performance, the work remains a theoretical contribution without clear debugging applications.

Coverage we drew on

DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data · arXiv cs.CL

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MentionsPythia · PolyPythia

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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Measuring Task-Agnostic Training Data Influence Across Language Model Pretraining”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.