How To Increase Plasticity in LLMs and AI Applications
SMRTR summary
AI models face a trade-off between retaining knowledge and learning new information. Researchers are developing techniques to mitigate loss of plasticity, allowing AI to continually learn without sacrificing performance. Methods like continual backpropagation and utility-based perturbed gradient descent show promise in addressing this challenge, potentially enabling AI to adapt indefinitely without costly retraining.
SMRTR provides this summary for quick context. The original article belongs to Daily.dev.
Read the original article