Unverified50% confidenceOpinionExact time
Active learning requires building a training-inference-sampling loop, and for a one-off small-scale task of 500 images, the setup cost may exceed the benefit.
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10/20/2026
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UnverifiedReinforcement learning requires massive numbers of interaction samples, so environment running speed and parallelization capability are critical for shortening training cycles77% similarUnverifiedReasoning models are extremely costly to train, typically requiring a combination of large-scale reinforcement learning and carefully designed reward models to acquire the capability.76% similarUnverifiedExperiments usually adopt a multi-round active learning loop: train the model, select samples, query labels, and retrain.76% similarUnverifiedTraining cost figures from a single case are highly dependent on the specific task, data scale, and base model, and should not be simply extrapolated to all scenarios.75% similar
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