# Feng and colleagues propose SCOPE for long-horizon Web tasks [Canonical reading](https://escapemeridian.com/readings/feng-and-colleagues-propose-scope-for-long-horizon-web-tasks-15805731) The paper’s authors, Xueyang Feng, Xiaohe Bo, Xu Chen, Quanyu Dai and Feng Wen, propose Skill-Composed Progressive Exploration (SCOPE) to help LLM Web agents learn long tasks despite sparse rewards and few successful attempts. Their approach builds from reusable local skills: it finds skills and failure experiences in earlier attempts, combines skills into chains, then uses relevant failures to guide exploration of additional subskills. The authors say SCOPE progressively builds task-completing skill chains across attempts and incorporates them into the agent’s policy through agentic on-policy distillation. In experiments on WebArena, WebVoyager and WebShop, they report relative gains of 3.9% to 18.6% over the strongest baselines in each setting. These results concern the reported benchmarks; the abstract does not establish how the approach performs on other websites or real-world tasks. The paper’s central idea is to use failures not just as unsuccessful outcomes, but as guidance for learning the next piece of a longer task. How well do the reported gains carry over to web tasks beyond the three tested benchmarks? Headline and summary by Escape Meridian, prepared by Luna from the attributed source. A source check is not independent confirmation of the source’s claims. ## Original source arXiv cs.AI · Article Original source date: Nov 2, 2026 (2026-11-02). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T06:32:47.184Z. First observed: 2026-11-03T09:04:00.965Z. Retrieved: 2026-11-04T03:32:46.637Z. - [From Local Skills to Long-Horizon Web Tasks: Progressive Skill Exploration for LLM Web Agents](https://arxiv.org/abs/2611.03116) ## Limits The supplied evidence is the abstract, so it does not provide experiment details, baseline identities, or results on tasks beyond WebArena, WebVoyager, and WebShop. ## Open question How well do the reported gains carry over to web tasks beyond the three tested benchmarks? ## Source-stated The authors propose SCOPE, which progressively explores from local skills toward complete task-solving capability for long-horizon Web agents. > we propose Skill-Composed Progressive Exploration (SCOPE), a short-to-long learning framework for long-horizon Web agents - [Supporting source](https://arxiv.org/abs/2611.03116) ## Source-stated SCOPE finds reusable local skills and failure experiences, composes skills into chains, and uses aligned failures to guide exploration of new subskills. > Skill Expansion leverages failure experiences aligned with the current skill chain to guide the agent toward exploring new subskills. - [Supporting source](https://arxiv.org/abs/2611.03116) ## Source-stated The authors report relative gains of 3.9% to 18.6% over the strongest baselines in each of three benchmark settings. > achieves 3.9% to 18.6% relative gains over the strongest baselines in each setting. - [Supporting source](https://arxiv.org/abs/2611.03116) ## Source and contributor context Feng, Xueyang, Bo, Xiaohe, Chen, Xu, Dai, Quanyu, Wen, Feng · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Agentic AI AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer. - [Definition reference](https://developers.google.com/machine-learning/glossary#agentic) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 656384a5-7daf-4c8f-888f-39c8e7aa42bc. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T06:32:47.184Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 853680bc26f2af4e34f7785fc9b88b11e8ab3bf14fccabc4951898052b8c801d. Source text hash: 31ceaf09f29f7110683c7940acf0a96bbe317bd9ed15f07a8c2f717a8ad15c38. ## Revision and source-check history ### Version 1 — 2026-11-10T06:32:47.184Z The paper’s authors, Xueyang Feng, Xiaohe Bo, Xu Chen, Quanyu Dai and Feng Wen, propose Skill-Composed Progressive Exploration (SCOPE) to help LLM Web agents learn long tasks despite sparse rewards and few successful attempts. Their approach builds from reusable local skills: it finds skills and failure experiences in earlier attempts, combines skills into chains, then uses relevant failures to guide exploration of additional subskills. The authors say SCOPE progressively builds task-completing skill chains across attempts and incorporates them into the agent’s policy through agentic on-policy distillation. In experiments on WebArena, WebVoyager and WebShop, they report relative gains of 3.9% to 18.6% over the strongest baselines in each setting. These results concern the reported benchmarks; the abstract does not establish how the approach performs on other websites or real-world tasks. The paper’s central idea is to use failures not just as unsuccessful outcomes, but as guidance for learning the next piece of a longer task. How well do the reported gains carry over to web tasks beyond the three tested benchmarks? ## Original source arXiv cs.AI · Article Original source date: Nov 2, 2026 (2026-11-02). Date basis: Date basis not provided. Added to Escape Meridian: 2026-11-10T06:32:47.184Z. First observed: 2026-11-03T09:04:00.965Z. Retrieved: 2026-11-04T03:32:46.637Z. - [From Local Skills to Long-Horizon Web Tasks: Progressive Skill Exploration for LLM Web Agents](https://arxiv.org/abs/2611.03116) ## Limits The supplied evidence is the abstract, so it does not provide experiment details, baseline identities, or results on tasks beyond WebArena, WebVoyager, and WebShop. ## Open question How well do the reported gains carry over to web tasks beyond the three tested benchmarks? ## Source-stated The authors propose SCOPE, which progressively explores from local skills toward complete task-solving capability for long-horizon Web agents. > we propose Skill-Composed Progressive Exploration (SCOPE), a short-to-long learning framework for long-horizon Web agents - [Supporting source](https://arxiv.org/abs/2611.03116) ## Source-stated SCOPE finds reusable local skills and failure experiences, composes skills into chains, and uses aligned failures to guide exploration of new subskills. > Skill Expansion leverages failure experiences aligned with the current skill chain to guide the agent toward exploring new subskills. - [Supporting source](https://arxiv.org/abs/2611.03116) ## Source-stated The authors report relative gains of 3.9% to 18.6% over the strongest baselines in each of three benchmark settings. > achieves 3.9% to 18.6% relative gains over the strongest baselines in each setting. - [Supporting source](https://arxiv.org/abs/2611.03116) ## Source and contributor context Feng, Xueyang, Bo, Xiaohe, Chen, Xu, Dai, Quanyu, Wen, Feng · Article author These are credits stated by the source; they do not establish a profile match or authorship of every passage. ## Source media No suitable media was found by the supported page extraction. ## Terms and references Reference context selected by Escape Meridian; separate from source evidence. Entity mentions do not establish authorship, involvement or endorsement. ## Large language model A language model with many learned numerical settings, used to process and generate language. - [Definition reference](https://developers.google.com/machine-learning/glossary#large-language-model) ## Agentic AI AI systems that plan and carry out a series of steps toward a goal, often by using other software, rather than giving one answer. - [Definition reference](https://developers.google.com/machine-learning/glossary#agentic) ## Benchmark A defined set of tests used to compare systems. Results depend on the tasks and measures selected. - [Definition reference](https://arxiv.org/abs/2211.09110) ## arXiv arXiv is a research manuscript repository where authors share papers that may not yet have been peer reviewed. - [Explore this context](/entities/arxiv) - [Reference](https://arxiv.org/) - [Reference](https://info.arxiv.org/help/api/index.html) ## Editorial provenance Version 1. Publication ID: 656384a5-7daf-4c8f-888f-39c8e7aa42bc. Prepared by gpt-6-luna; grounding review by gpt-6-luna. Source check: passed by anthropic/claude-opus-5.5 on 2026-11-10T06:32:47.184Z. This checks fidelity to the source, not whether its claims are independently true. Editorial selection: accept; reasons: material\_development. Observation: 853680bc26f2af4e34f7785fc9b88b11e8ab3bf14fccabc4951898052b8c801d. Source text hash: 31ceaf09f29f7110683c7940acf0a96bbe317bd9ed15f07a8c2f717a8ad15c38. ### passed — 2026-11-10T06:32:47.184Z — anthropic/claude-opus-5.5 Publication: 656384a5-7daf-4c8f-888f-39c8e7aa42bc attribution — pass: The reading correctly names all five listed authors and attributes the framework, mechanism and benchmark results to them as their proposal and reported findings, not as established fact. > Authors:Xueyang Feng, Xiaohe Bo, Xu Chen, Quanyu Dai, Feng Wen summary — pass: The reading leads with the actor and proposal, explains the three-stage mechanism in plain language, and keeps the benchmark scope clear. The closing question about generalization follows from the evidence. Exact terms such as sparse rewards, long-horizon, policy and agentic on-policy distillation should rely on term-library tooltips; add entries if missing. Omitting the skill acquisition theory inspiration and the NeurIPS acceptance does not distort meaning. > decomposes complex task exploration into a gradual expansion process from local skills to complete task-solving capability date — pass: originalPublished 2026-11-02 matches the v1 arXiv submission date. It is not the later feed date of 2026-11-03. > \[Submitted on 2 Nov 2026\] support — pass: Each claim is supported by the abstract. The motivation, the mechanism (discovery, orchestration and expansion), the internalization into the policy, and the 3.9% to 18.6% relative gains are all source-stated. The idea of using failures as guidance is grounded in Skill Expansion. The reading correctly limits the results to the three benchmarks and does not imply real-world reliability. > leverages failure experiences aligned with the current skill chain to guide the agent toward exploring new subskills media — pass: The selected media is the verified arXiv abstract page for this paper. It is the direct original source and its ID matches the decision. ## Recorded references Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter. No public references recorded here yet. [Reference data (JSON)](/readings/158057316084d0e66ec8707b81767260cdcf7b368ccf4cba6da9bd81d9591e84/references.json) ## Corrections and challenges [Send a correction or an opposing source](https://escapemeridian.com/corrections/) [JSON record](https://escapemeridian.com/readings/feng-and-colleagues-propose-scope-for-long-horizon-web-tasks-15805731.json)