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Briefings and ideas about Catastrophic forgetting from AI podcast discussions and the research they cite, with links to the original sources.
Goodfire's Eric Bigelow argues in-context learning covers nearly everything a model does to adapt as it reads. He expects context, memory and harnesses, not per-user fine-tuning, to drive personalization, but hasn't researched continual learning.
Applied Compute CEO Yash Patil says reinforcement learning optimizes well toward a set target, but defining it is hard. He says RL transfers far less than pre-training and that data-efficient continual learning is still unsolved.
Charlie O'Neill told the Dwarkesh Podcast that AI already learns from deployment, but in slow batches, and that many small updates erase earlier learning; John Schulman expects customers to resist feeding a shared model.
Rich Sutton and Khurram Javed's Oak Lab wants deployed AI to update its weights from individual experience, using per-weight learning rates and continual backpropagation—backed by earlier experiments, not yet a demonstrated general-purpose system.