TodoEvolve: Learning to Architect Agent Planning Systems
Planning has become a central capability for contemporary agent systems in navigating complex, long-horizon tasks, yet existing approaches predominantly rely on fixed, hand-crafted planning structures that lack the flexibility to adapt to the structural diversity of open-ended problems. To address this limitation, we introduce TodoEvolve, a meta-planning paradigm that autonomously synthesizes and dynamically revises task-specific planning architectures. Specifically, we first construct PlanFactory, a modular design space that standardizes diverse planning paradigms within a unified codebase encompassing topology, initialization, adaptation, and navigation, thereby providing a common interface for heterogeneous planning patterns. Leveraging PlanFactory, we collect high-quality planning trajectories and train Todo-14B via Impedance-Guided Preference Optimization (IGPO), a multi-objective reinforcement learning objective that encourages the generation of planning systems that are performant, stable, and token-efficient across arbitrary tasks and agent backbones. Empirical evaluations on five agentic benchmarks demonstrate that TodoEvolve consistently surpasses carefully engineered planning modules while maintaining economical API costs and runtime overhead.
698b1fc81b2dc6b37d61b42b
Jiaxi Liu
698b1fc81b2dc6b37d61b42c
Yanzuo Jiang
698b1fc81b2dc6b37d61b42d
Guibin Zhang
698b1fc81b2dc6b37d61b42e
Zihan Zhang
698b1fc81b2dc6b37d61b42f
Heng Chang
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2026-03-23T11:01:19.174Z
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Heng Chang
hchang95
user
hchang95
698b1fc81b2dc6b37d61b430
Zhenfei Yin
698b1fc81b2dc6b37d61b431
Qibing Ren
698b1fc81b2dc6b37d61b432
Junchi Yan
TodoEvolve: Learning to Architect Agent Planning Systems
Planning has become a central capability for contemporary agent systems in navigating complex, long-horizon tasks, yet existing approaches predominantly rely on fixed, hand-crafted planning structures that lack the flexibility to adapt to the structural diversity of open-ended problems. To address this limitation, we introduce TodoEvolve, a meta-planning paradigm that autonomously synthesizes and dynamically revises task-specific planning architectures. Specifically, we first construct PlanFactory, a modular design space that standardizes diverse planning paradigms within a unified codebase encompassing topology, initialization, adaptation, and navigation, thereby providing a common interface for heterogeneous planning patterns. Leveraging PlanFactory, we collect high-quality planning trajectories and train Todo-14B via Impedance-Guided Preference Optimization (IGPO), a multi-objective reinforcement learning objective that encourages the generation of planning systems that are performant, stable, and token-efficient across arbitrary tasks and agent backbones. Empirical evaluations on five agentic benchmarks demonstrate that TodoEvolve consistently surpasses carefully engineered planning modules while maintaining economical API costs and runtime overhead.
TodoEvolve enables autonomous synthesis and revision of task-specific planning architectures through a modular design space and multi-objective reinforcement learning optimization.
meta-planning planning architectures PlanFactory Impedance-Guided Preference Optimization IGPO reinforcement learning task-specific planning modular design space