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FlowEvo enables agents to continuously evolve their skills and workflows on-the-fly, leading to unprecedented performance gains in complex task execution.
Intrinsic reward signals in unsupervised RL for LLMs inevitably collapse due to sharpening of the model's prior, but external rewards grounded in computational asymmetries offer a path to sustained scaling.
