LLMs Encode How Difficult Problems Are
SMRTR summary
Large language models internally encode problem difficulty that closely matches human judgment, with researchers finding this representation can be decoded with 88% correlation and strengthens during reinforcement learning training. Steering models toward "easier" internal representations reduces errors and improves accuracy, while automated difficulty estimates become less reliable as models improve.
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