Greedy Local Learning for Language Model Pretraining: Gaps and Objective Design
Greedy block-wise local learning splits a network into gradient-isolated blocks trained by local auxiliary losses, deleting the backward pass between blocks: inter-stage communication becomes forward-only and every block can step its optimizer independently, properties directly relevant to decentralized model-parallel training. Local learning is competitive with end-to-end backpropagation on image classification, and on small Transformers it is known to trade a worse best loss for parallel speedup. How this loss gap behaves in autoregressive language model (LM) pretraining at larger scale, and which auxiliary designs reduce it, has not been measured. We present a token-budget-matched empi...
| Authors | Jihwan Moon, Sheir A. Zaheer, Jinmyoung Lee et al. |
|---|---|
| Primary category | cs.LG |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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