{"work":{"id":"ee84a785-1e36-4af7-b0e8-13fc86cba1ea","openalex_id":null,"doi":null,"arxiv_id":"2503.09572","raw_key":null,"title":"Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks","authors":null,"authors_text":"Lutfi Eren Erdogan, Nicholas Lee, Sehoon Kim, Suhong Moon, Hiroki Furuta, Gopala Anumanchipalli","year":2025,"venue":"cs.CL","abstract":"Large language models (LLMs) have shown remarkable advancements in enabling language agents to tackle simple tasks. However, applying them for complex, multi-step, long-horizon tasks remains a challenge. Recent work have found success by separating high-level planning from low-level execution, which enables the model to effectively balance high-level planning objectives and low-level execution details. However, generating accurate plans remains difficult since LLMs are not inherently trained for this task. To address this, we propose Plan-and-Act, a novel framework that incorporates explicit planning into LLM-based agents and introduces a scalable method to enhance plan generation through a novel synthetic data generation method. Plan-and-Act consists of a Planner model which generates structured, high-level plans to achieve user goals, and an Executor model that translates these plans into environment-specific actions. To train the Planner effectively, we introduce a synthetic data generation method that annotates ground-truth trajectories with feasible plans, augmented with diverse and extensive examples to enhance generalization. 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.","external_url":"https://arxiv.org/abs/2503.09572","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-05-20T10:58:14.461972+00:00","pith_arxiv_id":"2503.09572","created_at":"2026-05-11T06:41:36.927146+00:00","updated_at":"2026-06-05T21:23:00.469572+00:00","title_quality_ok":true,"display_title":"Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks","render_title":"Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks"},"hub":{"state":{"work_id":"ee84a785-1e36-4af7-b0e8-13fc86cba1ea","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":18,"external_cited_by_count":null,"distinct_field_count":5,"first_pith_cited_at":"2025-11-12T03:48:05+00:00","last_pith_cited_at":"2026-05-18T17:59:03+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-06-09T16:55:33.604622+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":12},{"context_role":"baseline","n":1}],"polarity_counts":[{"context_polarity":"background","n":12},{"context_polarity":"baseline","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}