Progressive Agent Skill Generation via Reinforcement Learning

Hugging Face Daily Papers Papers
reinforcement-learning skill-generation llm-agents agent-skills

Summary

Introduces Skill-α, a reinforcement learning method for progressively generating high-quality agent skills by treating skill generation as sequential editing with a rollback reward. It improves downstream success rates over existing baselines on CL-Bench and tau2-bench.

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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Paper page - Progressive Agent Skill Generation via Reinforcement Learning

Source: https://huggingface.co/papers/2608.01678

Abstract

Existingskillgenerationmethodslargelyrelyonheuristicsorpipeline-styleconsolidation,whichmustbespeciallydesignedfordifferentevidencesources.Incontrast,learning-basedapproachesofferamoreunifiedwaytomodelskillgenerationacrossheterogeneoussources.However,learning-basedskillgenerationremainschallengingbecauseskillslackanaturalsupervisionsignalbasedonrelevanceorcorrectness;theirvaluecanlargelybedeterminedonlybywhethertheyimprovethebehavioroftheagentondownstreamtasks.Toaddressthischallenge,weproposeSkill-α,areinforcementlearningmethodforprogressivelygeneratinghigh-qualityagentskills.Specifically,weformulateskillgenerationasasequentialeditingprocessthatdecomposesskillconstructionintoindividuallyevaluableedits,andintroduceanovelrollbackrewardthatevaluateseacheditbycomparingdownstreamexecutionundertheoriginalandeditedskillsonananchoredquery.ExtensiveexperimentsshowthatSkill-αgeneratesmoreeffectiveskillsthanmethodsbasedonheuristicsorpipelinesinbothdocument-to-skillandexperience-to-skillsettings.UnderthemainGPT-4oworker,Skill-αimprovesaveragedownstreamsuccessratesoverthestrongestskill-generationbaselineby3.3pointsonCL-Benchand6.7pointsontau2-bench.Furtherablationsvalidatetheimportanceofrollbackrewardandprogressivegeneration.

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