Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training
For practitioners fine-tuning large language models, LoPT offers a cheaper and faster alternative to standard post-training, reducing memory and computational costs while preserving pretrained capabilities.
LLM post-training typically uses full-depth backpropagation, which is expensive and intrusive. LoPT introduces a gradient boundary at the transformer midpoint, updating the second half with task loss and the first half with a lightweight reconstruction objective, achieving competitive performance with lower memory and higher efficiency.
LLM post-training typically propagates task gradients through the full depth of the model. Although this end-to-end structure is simple and general, it couples task adaptation to full-depth activation storage, long-range backward dependencies and direct task-gradient access to pretrained representations. We argue that this full-depth backward coupling can be unnecessarily expensive and intrusive, particularly when post-training supervision is much narrower than pre-training. To this end, we propose \textbf{LoPT}: Local-Learning Post-Training, a simple post-training strategy that makes gradient reach an explicit design choice. LoPT places a single gradient boundary at the transformer midpoint: the second-half block learns from the task objective, while the first-half block is updated by a lightweight feature-reconstruction objective to preserve useful representations and maintain interface compatibility. LoPT shortens the task-induced backward path while limiting direct interference from narrow task gradients on early-layer representations. Extensive experiments demonstrate that LoPT achieves competitive performance with lower memory cost, higher training efficiency and better retention of pretrained capabilities. Our code is available at: https://github.com/HumyuShi/LoPT