Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training

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 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 where the upper block learns from the task objective and the lower block is updated by a lightweight feature-reconstruction objective to preserve useful representations and maintain interface compatibility. Such design shortens the task-induced backward path while limiting direct interference from narrow task gradients on lower-layer representations. Extensive experiments demonstrate that LoPT achieves competitive performance with lower memory cost, higher training efficiency and better retention of pretrained capabilities.