MetaDecomp: Adaptive Task Decomposition via Meta-Learned Procedural Memory

Reusing past experience in long-horizon tasks requires more than storing successful procedures: Language agents must distinguish which procedures apply and what work remains as execution progresses. We introduce MetaDecomp, an in-context meta-learning framework for adaptive task decomposition that jointly learns a task-relative state space and reusable procedures. Its outer loop builds procedural memory from successful training executions, representing task progress as abstract states and procedures between states as directed acyclic graphs of operations and dependencies. As new progress distinctions are learned, MetaDecomp revisits historical execution evidence to reassess state assignments and reorganize the procedures they support. Its inner loop uses this memory to refine an initial decomposition and revise it using environmental feedback, combining applicable procedure fragments with the work required by the current task. Evaluated on ALFWorld and ScienceWorld over three seeds, MetaDecomp achieves the highest mean performance among the compared methods in four of five settings, for example, an 86.3% success rate on ALFWorld with Qwen3-4B. These results are achieved with competitive inference costs across all five settings. Ablation results show that learned cross-task procedural knowledge improves mean performance over within-task revision alone in every setting. Code will be released.