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drift-constrained-optimization

topic1 events
papersTODAY 04:00 UTC

Drift-Constrained Optimization Targets Direction Over Magnitude in LLM Fine-Tuning

A new arXiv paper argues that fine-tuning instruction-tuned models can improve target tasks while causing unwanted behavioral drift away from the reference model, which may erode existing abilities. The authors propose a drift-constrained optimization approach in which the direction of parameter updates, rather than their size, is what governs this divergence. Treating drift as a controlled constraint instead of an incidental byproduct of training is the paper's central framing.

fine-tuning/llm/optimization/arxiv