Large-Scale Data Selection for Instruction Tuning

Analysis

Why This Paper Matters

This paper addresses a critical bottleneck in instruction-tuning large language models (LLMs): how to efficiently select the most effective training data from large pools. As LLMs grow and instruction datasets expand, naive random selection becomes increasingly suboptimal, yet many sophisticated automated methods have been proposed without rigorous scaling analysis. The finding that many existing methods underperform random selection at larger scales is both surprising and practically important, as it suggests that current heuristics may not generalize.

The introduction of RDS+, a representation-based method that consistently outperforms alternatives, provides a concrete solution that works across scales and multi-task settings. This is significant for practitioners who need reliable data selection pipelines that do not degrade as dataset sizes grow.

Technical Contributions

• Systematic scaling analysis: The paper evaluates data selection methods across multiple dataset sizes, revealing that performance of many methods degrades relative to random selection at larger scales.
• RDS+ method: A variant of representation-based data selection that uses weighted mean pooling of pre-trained LM hidden states, rather than simple averaging or last-token pooling.
• Multi-task validation: RDS+ is shown to be effective not only in single-task but also in multi-task instruction-tuning scenarios, increasing its practical applicability.
• Empirical benchmarking: Provides a clear comparison of multiple automated selection methods (e.g., diversity-based, difficulty-based, representation-based) against random baselines at scale.

Results

The abstract reports that many existing automated data selection methods underperform random selection when applied at larger scales. In contrast, RDS+ consistently outperforms all other methods, including random selection, across scales. The method also demonstrates strong performance in multi-task settings, suggesting robustness to task diversity. Specific numerical metrics (e.g., accuracy, BLEU, or task-specific scores) are not provided in the abstract, but the consistent outperformance is highlighted as the key result.

Significance

This work has immediate implications for AI practitioners building instruction-tuned LLMs. It warns against blindly applying popular data selection methods without scale-aware validation, and offers a reliable alternative in RDS+. The finding that random selection is competitive at scale also suggests that simpler baselines should be more rigorously compared in future research. Broader impacts include more efficient use of computational resources for data curation and potentially better-performing instruction-tuned models with less manual effort.