Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs

Shane Bergsma; Nolan Dey; Joel Hestness

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arXiv · 2509.25380

Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs

Shane Bergsma·Nolan Dey·Joel Hestness

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Abstract

Data curriculums have become central to successful LLM training, yet principles governing optimal data placement remain unclear. We introduce the *training re-evaluation curve (TREC)*, a diagnostic that retrospectively evaluates training batches *using the final model weights*. The TREC characterizes how well a trained model retains training data as a function of *when* the data was encountered during training. Analyzing TRECs for models from 111M to 3.9B parameters, we show that placing high-quality data at low points on the TREC significantly improves performance. Importantly, while a TREC is initially observable only after training, we demonstrate it can be *predicted in advance* from AdamW's implicit EMA coefficients, enabling proactive curriculum design. By predicting TRECs for published training recipes, we explain prior ablations and reveal suboptimal data placements. We also align high-quality data with TREC minima in order to improve continual pre-training of a 3.9B-parameter LLM trained on 900B tokens.

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Shane Bergsma, Nolan Dey, Joel Hestness. 2025-09-29. Predicting Training Re-evaluation Curves Enables Effective Data Curriculums for LLMs. https://arxiv.org/abs/2509.25380

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