REVIEW 4 cited by
CAME: Confidence-guided Adaptive Memory Efficient Optimization
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Adaptive gradient methods, such as Adam and LAMB, have demonstrated excellent performance in the training of large language models. Nevertheless, the need for adaptivity requires maintaining second-moment estimates of the per-parameter gradients, which entails a high cost of extra memory overheads. To solve this problem, several memory-efficient optimizers (e.g., Adafactor) have been proposed to obtain a drastic reduction in auxiliary memory usage, but with a performance penalty. In this paper, we first study a confidence-guided strategy to reduce the instability of existing memory efficient optimizers. Based on this strategy, we propose CAME to simultaneously achieve two goals: fast convergence as in traditional adaptive methods, and low memory usage as in memory-efficient methods. Extensive experiments demonstrate the training stability and superior performance of CAME across various NLP tasks such as BERT and GPT-2 training. Notably, for BERT pre-training on the large batch size of 32,768, our proposed optimizer attains faster convergence and higher accuracy compared with the Adam optimizer. The implementation of CAME is publicly available.
Forward citations
Cited by 4 Pith papers
-
Low-rank Momentum Factorization for Memory Efficient Training
MoFaSGD keeps a low-rank factored momentum and uses its singular vectors as the update direction, achieving LoRA-level memory with competitive fine-tuning performance, but its convergence proof is flawed.
-
AdamS: Momentum Itself Can Be A Normalizer for LLM Pretraining and Post-training
AdamS replaces AdamW's second-moment storage with a momentum-and-gradient squared denominator, matching AdamW's loss curves with half the optimizer memory.
-
Sparse Fine-Tuning of Transformers for Generative Tasks
A frozen transformer is fine-tuned by adding a sparse dictionary of feature atoms to each layer's output, enabling atom-level control that improves image editing and concept customization.
-
Taming LLMs by Scaling Learning Rates with Gradient Grouping
An optimizer wrapper that clusters per-layer momentum and scales learning rates by cluster-wise median deviations improves perplexity and accuracy across LLM and MLLM training, and lets LoRA pretraining approach full-...
Discussion (0). Sign in to comment.