arXiv:2607.01455v1 Announce Type: new Abstract: Language models learn continuous programs over discrete symbols, with the embedding table and LM-head acting as the read/write interface between them. We show that this interface has gradient geometry distinct from dense hidden weights which can be exploited to improve the Pareto frontier across supervised finetuning, RL, and pretraining, while only utilizing kilobytes of optimizer state. We introduce Ember, a lightweight optimizer for embedding and LM-head matrices that utilizes O(V + D) VRAM, instead of Adam's O(2VD), and forgoes the need to sh
The continuous growth in language model size necessitates more efficient optimization techniques for both training and deployment, pushing innovation in this area.
Improving the efficiency of language model training and finetuning can significantly reduce computational resource requirements, democratizing access and accelerating development.
Optimization of large language models may become less computationally intensive, potentially lowering the barrier to entry for model development and deployment.
- · AI researchers and developers
- · Cloud computing providers (reduced egress costs)
- · Startups developing custom LLMs
- · Hardware manufacturers (new optimization targets)
- · Inefficient optimizer developers
Reduced VRAM consumption and optimizer state for LLM training and finetuning.
Faster iteration cycles for AI model development and potentially more diverse model architectures become feasible.
Enhanced competition in the LLM space as smaller entities can more easily train and adapt models, leading to a proliferation of specialized AI agents.
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Read at arXiv cs.LG