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This work uses teacher-successful problems to define a cheap reference for what the student can learn and measures how the likelihood of each observed token in trajectories from teacher-failed problems changes as an operational learnability signal, which can be computed once from stored trajectories and model checkpoints.
Forget clunky pipelines: this multi-agent system crafts compelling short dramas from a single sentence, nailing narrative pacing and spatial consistency in ways LLMs alone can't.
