2 links tagged with all of: recursive self improvement + ai + optimization
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Human intelligence is a recent evolutionary accident, not an optimized endpoint—and now that we can explicitly define and measure intelligence, AI systems will outpace us wherever we can formalize the task. The real problem isn't staying competitive; it's choosing which objectives are worth optimizing for, since the definition of intelligence shapes what gets built.
- Evolution is a lazy optimizer that never selected humans for maximum intelligence; AI can reach superhuman performance in months once a task is formally specified, as AlphaGo demonstrated.
- Recursive self-improvement becomes possible once the model development process itself is broken into measurable subtasks—meaning even the optimization process will eventually be automated.
- How we measure and evaluate intelligence directly shapes what intelligence emerges; a system rewarded for stock market gains develops differently than one optimized for curing cancer, and poorly defined objectives can produce capable but undesirable behavior.
- The burden shifts to humans defining which hills are worth climbing rather than competing on every one—choosing the right objective matters more than winning the optimization race.
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recursive self improvement
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This paper presents Dream-RSI, a framework that lets AI agents improve their exploration strategies by learning from past discovery attempts without constantly running expensive new experiments. The system uses historical search data as a simulator to test and refine exploration policies cheaply before deploying them back into the real search space.
- Solves the exploration bottleneck in recursive self-improvement by creating a replay simulator from accumulated discovery history, enabling off-policy feedback without costly online re-evaluation
- Keeps the exploration layer separate from the underlying agent, making strategies explicit and programmable while maintaining compatibility with existing systems
- Demonstrates competitive or better results across algorithm engineering, mathematical optimization, and GPU kernel engineering while substantially cutting discovery costs