A recursive self-training loop that filters prompts, selects preferred images, and reweights out-of-distribution samples improves Stable Diffusion models over multiple rounds.
Bounded Recursive Self-Improvement
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abstract
We have designed a machine that becomes increasingly better at behaving in underspecified circumstances, in a goal-directed way, on the job, by modeling itself and its environment as experience accumulates. Based on principles of autocatalysis, endogeny, and reflectivity, the work provides an architectural blueprint for constructing systems with high levels of operational autonomy in underspecified circumstances, starting from a small seed. Through value-driven dynamic priority scheduling controlling the parallel execution of a vast number of reasoning threads, the system achieves recursive self-improvement after it leaves the lab, within the boundaries imposed by its designers. A prototype system has been implemented and demonstrated to learn a complex real-world task, real-time multimodal dialogue with humans, by on-line observation. Our work presents solutions to several challenges that must be solved for achieving artificial general intelligence.
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cs.CV 1years
2025 1verdicts
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Generating on Generated: An Approach Towards Self-Evolving Diffusion Models
A recursive self-training loop that filters prompts, selects preferred images, and reweights out-of-distribution samples improves Stable Diffusion models over multiple rounds.