Hugging Face Daily Papers · August 4, 2026 · 3 min read

Progressive Agent Skill Generation via Reinforcement Learning

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Code is available at <a href=\"https://github.com/ejhshen/skill-alpha\" rel=\"nofollow\">https://github.com/ejhshen/skill-alpha</a></p>\n","updatedAt":"2026-08-04T04:58:31.572Z","author":{"_id":"687f853bb39262ba84f3eeff","avatarUrl":"/avatars/cdfc44fde8237f08f10192553fe5a075.svg","fullname":"Junhao Shen","name":"shenjunhao","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"followerCount":4,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.6516523957252502},"editors":["shenjunhao"],"editorAvatarUrls":["/avatars/cdfc44fde8237f08f10192553fe5a075.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2608.01678","authors":[{"_id":"6a7162c9ec5082b9f872ceaf","name":"Junhao Shen","hidden":false},{"_id":"6a7162c9ec5082b9f872ceb0","name":"Zhanqiu Zhang","hidden":false},{"_id":"6a7162c9ec5082b9f872ceb1","name":"Yiwen Guo","hidden":false},{"_id":"6a7162c9ec5082b9f872ceb2","name":"Hong Cheng","hidden":false}],"publishedAt":"2026-08-03T00:00:00.000Z","submittedOnDailyAt":"2026-08-04T00:00:00.000Z","title":"Progressive Agent Skill Generation via Reinforcement Learning","submittedOnDailyBy":{"_id":"687f853bb39262ba84f3eeff","avatarUrl":"/avatars/cdfc44fde8237f08f10192553fe5a075.svg","isPro":false,"fullname":"Junhao Shen","user":"shenjunhao","type":"user","name":"shenjunhao"},"summary":"Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. 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Papers

arxiv:2608.01678

Progressive Agent Skill Generation via Reinforcement Learning

Published on Aug 3

· Submitted by

Junhao Shen on Aug 4

#3 Paper of the day

The Chinese University of Hong Kong - Database Group

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Abstract

Existing skill generation methods largely rely on heuristics or pipeline-style consolidation, which must be specially designed for different evidence sources. In contrast, learning-based approaches offer a more unified way to model skill generation across heterogeneous sources. However, learning-based skill generation remains challenging because skills lack a natural supervision signal based on relevance or correctness; their value can largely be determined only by whether they improve the behavior of the agent on downstream tasks. To address this challenge, we propose Skill-α, a reinforcement learning method for progressively generating high-quality agent skills. Specifically, we formulate skill generation as a sequential editing process that decomposes skill construction into individually evaluable edits, and introduce a novel rollback reward that evaluates each edit by comparing downstream execution under the original and edited skills on an anchored query. Extensive experiments show that Skill-α generates more effective skills than methods based on heuristics or pipelines in both document-to-skill and experience-to-skill settings. Under the main GPT-4o worker, Skill-α improves average downstream success rates over the strongest skill-generation baseline by 3.3 points on CL-Bench and 6.7 points on tau2-bench. Further ablations validate the importance of rollback reward and progressive generation.

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