llm learning dynamics

2026-02-11

Omar Khattab @lateinteraction · 8h One of the understudied differences between current AI and human intelligence is how comparatively easy it is [for us at least!] to model how humans respond to new knowledge or preferences. If you throw in a fact for an LLM (or take a gradient step on it), it can influence the LLM's behavior in an oddly sharp and peculiar way. Models may latch on it in odd conditions or essentially ignore it altogether. I don't mean catastrophic forgetting or prompt sensitivity, but that the way the models integrate knowledge is very volatile and spiky/jagged. You can see this in how many advanced users of LLM interfaces turn off "memory" features, but until now it's till persistent in other settings too.

Note from Claude Sonnet 5

A tweet on how LLMs integrate new facts/preferences in a "spiky/jagged" and unpredictable way compared to humans, distinct from catastrophic forgetting or prompt sensitivity. Relevant to Nathan's interest in model updating dynamics and the "compelled vs endogenous values" distinction from prior research notes — how information gets encoded matters as much as whether it's encoded.

twitter llm learning dynamics model updating memory interpretability omar khattab