pith:63CLJGZZ
Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
Plan-and-Act improves LLM agent performance on long-horizon tasks by separating planning from execution and training the planner with synthetic data.
arxiv:2503.09572 v3 · 2025-03-12 · cs.CL
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Claims
We evaluate Plan-and-Act using web navigation as a representative long-horizon planning environment, demonstrating a state-of-the-art 57.58% success rate on the WebArena-Lite benchmark as well as a text-only state-of-the-art 81.36% success rate on WebVoyager.
That annotating ground-truth trajectories with feasible plans and augmenting with diverse synthetic examples will produce plans that generalize to unseen tasks and environments without overfitting to the annotation process.
Plan-and-Act trains a dedicated Planner on synthetic plan-annotated trajectories to generate high-level plans that an Executor follows, reaching 57.58% success on WebArena-Lite and 81.36% on WebVoyager.
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| First computed | 2026-05-17T23:38:13.022511Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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· · · · ·Agent API
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Canonical record JSON
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