Research map / Continual learning / Continual fine-tuning of LLMs

Effect of scale on catastrophic forgetting in neural networks

ICLR 2022 · Continual learning › Continual fine-tuning of LLMs · approach: Continual pretraining of language models

Read the paperCheck your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Closest papers by meaning

• The Importance of Being Lazy: Scaling Limits of Continual Learning (ICML 2025 · similarity 0.91)
• On the Diminishing Returns of Width for Continual Learning (ICML 2024 · similarity 0.90)
• Towards Understanding Catastrophic Forgetting in Two-layer Convolutional Neural Networks (ICML 2025 · similarity 0.90)
• Measuring Representational Shifts in Continual Learning: A Linear Transformation Perspective (ICML 2025 · similarity 0.90)
• Kolmogorov-Arnold Networks Still Catastrophically Forget but Differently from MLP (AAAI 2025 · similarity 0.89)
• Unlocking the Power of Function Vectors for Characterizing and Mitigating Catastrophic Forgetting in Continual Instruction Tuning (ICLR 2025 · similarity 0.88)