Approximating Gradient-Based Influence for Scalable Instruction Data Selection
Bibliographic record
Abstract
Instruction Tuning (IT) is crucial for enhancing Large Language Models (LLMs), but training on all available instructions is often unnecessary and computationally costly. Recent studies show that small, well-chosen subsets can match or exceed full dataset performance, motivating efficient data selection techniques. While gradient-based methods like LESS estimate sample influence effectively, they are expensive due to per-sample gradient computation. We propose Approx-LESS, a scalable alternative that computes LoRA-based gradient features for a small fraction of the samples and trains regression models to predict influence scores for the rest. This enables selection and tuning on the most impactful samples. On three validation sets with a fixed 270K instruction corpus, Approx-LESS outperforms applicable baselines and closely matches LESS, reducing gradient extraction time by over 3x. It also shows high sample selection overlap with LESS, making it an effective, low-cost method for influence-based instruction tuning.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".