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LESSER: Post-Training Data Selection with Output-Layer Gradients | AI Learning Platform

BenchmarkFine-tuningCost Optimization

LESSER: Post-Training Data Selection with Output-Layer Gradients

Lyuxin David Zhang, Eric Wong, Surbhi Goel et al.1 min readarXivOct 2, 2026

Key Takeaway for Students

The choice of post-training data for large language models substantially affects downstream performance. Gradient-based data selection is a popular approach that ranks training data by how well their gradients align with those of a small validation set. However, ranking with full-parameter gradients requires an expensive backward pass on every sample, making computation intract

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This paper introduces LESSER, a method for efficient post-training data selection that significantly reduces computational costs for large language models. By leveraging output-layer gradients, ML engineers can optimize data selection for fine-tuning without the prohibitive expense of full-parameter gradients, leading to more efficient model development and deployment.

Read original on arXiv

Career Insight

high demand

Why it matters

Skills to learn

Understanding of gradient-based data selection techniquesFamiliarity with forward and backward pass computations in deep learningAbility to analyze computational complexity (FLOPs) of ML operationsImplementing data selection pipelines for fine-tuning LLMsOptimizing training data for specific downstream tasks

Interview questions

Explain the trade-offs between using full-parameter gradients versus output-layer gradients for data selection in the context of LLM fine-tuning.
How would you adapt LESSER for a scenario where the validation set is extremely small or noisy?
Design a system for continuously selecting and updating training data for a production LLM, incorporating a method like LESSER to manage computational costs.

Relevant for ~20 months · generated by the enrichment pipeline