Summary of Ldadam: Adaptive Optimization From Low-dimensional Gradient Statistics, by Thomas Robert and Mher Safaryan and Ionut-vlad Modoranu and Dan Alistarh
LDAdam: Adaptive Optimization from Low-Dimensional Gradient Statistics
by Thomas Robert, Mher Safaryan, Ionut-Vlad Modoranu, Dan Alistarh
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A memory-efficient optimizer, LDAdam, is introduced for training large models. This optimizer performs adaptive optimization steps within lower-dimensional subspaces while consistently exploring the full parameter space during training, keeping the memory footprint to a fraction of the model size. The new projection-aware update rule allows for transitioning between subspaces, estimating the statistics of projected gradients. A generalized error feedback mechanism mitigates errors due to low-rank projection. LDAdam converges under standard assumptions and enables accurate and efficient fine-tuning and pre-training of language models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LDAdam is a new way to train big computer models that uses less memory. It works by breaking down the model into smaller parts, solving one part at a time, and then putting all the pieces back together. This helps keep track of how well the model is doing while using much less memory than usual. The new method also helps fix mistakes made during this process. LDAdam is tested on language models and shown to be effective for fine-tuning and pre-training. |
Keywords
» Artificial intelligence » Fine tuning » Optimization
