Composed labels beat whole-problem guesses: compositional self-improvement pushes reliable prediction past a model's starting range
Models struggle to generate reliable supervision for harder, out-of-distribution instances, so compositional self-improvement instead predicts simpler subproblems and combines them into labels for full problems, training on those labels repeatedly. Across three controlled algorithmic tasks, composed labels extended reliable prediction beyond the initial range while direct self-improvement gave little or no such extension on addition and grid navigation; program generation and multi-hop reasoning likewise showed composed labels more accurate than direct self-labels, though unreliable components, invalid composition rules and limited coverage cap the gains.