arXiv:2606.10406v1 Announce Type: new Abstract: We argue that forgetting is not confined to continual learning but is a general optimization phenomenon: during standard training, dominant mini-batch gradients suppress rare but useful update directions, causing short-term forgetting at every step. When such knowledge is never revisited, these losses compound into long-term forgetting-the classical failure mode of continual learning. We introduce FOGO, a scalable optimizer that continuously detects and resolves gradient interference across both regimes. FOGO spectrally orthogonalizes momentum up
The continuous drive for more efficient and robust AI models highlights the persistent challenges in current optimization techniques, making innovations like FOGO timely as model complexity grows.
This development offers a potential breakthrough in AI optimization, improving model stability and performance by addressing 'forgetting' during training, which could accelerate AI development and deployment.
Standard AI training might become more efficient and less prone to 'short-term forgetting,' yielding more reliable and capable models without requiring fundamental architectural changes to existing neural networks.
- · AI researchers
- · Machine learning engineers
- · Cloud AI providers
- · Companies deploying complex AI models
- · Current less efficient optimization algorithms
- · AI projects frequently requiring extensive re-training due to instability
AI models across various applications demonstrate improved stability and learning efficiency.
Reduced computational costs for training and maintaining high-performance AI systems become possible.
More sophisticated and continuously learning AI agents become feasible, redefining automation capabilities.
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Read at arXiv cs.LG