Dynamic Data Strategies for Fine-Tuning When Compute is Tight
Puzzleheaded_Box2842 · reddit · 2026-08-11
When fine-tuning budgets are limited, the core question is how to maximize the value of each compute update. The author outlines three dynamic data strategies:
- Dynamic Selection: For oversized datasets. The trainer periodically evaluates which samples are worth the next window of compute (loss-based, gradient-based, etc.), ideal for noisy SFT data.
- Dynamic Mixing: For multi-domain data. Instead of fixed ratios, the system dynamically adjusts source proportions (wiki, code, math) during training, useful for continued pretraining.
- Dynamic Weighting: Keeps all samples but applies different loss weights before backprop, softly emphasizing high-value examples without dropping data.
This design is implemented in the open-source project OpenDCAI/DataFlex, plugging directly into the LLaMA-Factory training loop.
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