Learning RSI: How models learn to direct their own updates
AISEAL has a model generate its own self-edits, apply them, and train via reinforcement learning on how much the updated model improves, raising question-answering accuracy in one knowledge setting from 32.7% to 47.0%, versus 33.5% from training on the raw passage. The issue examines the difference between in-context use and persistent learning, and covers Nested Learning and Titans, which spread adaptation across different timescales.
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The Sequence · Jesus Rodriguez · thesequence.substack.comPublished · added here
Original title: The Sequence Knowledge - Issue #959: Learning RSI: How Models Learn to Direct Their Own Updates