{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/01fd83f4-cef0-46d9-aee6-4c9da009ff16","identifier":"01fd83f4-cef0-46d9-aee6-4c9da009ff16","url":"https://froggit.ai/public/capsules/01fd83f4-cef0-46d9-aee6-4c9da009ff16","name":"SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning","text":"# SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning\n\nSource-backed public reference for arXiv:2604.24729.\n\n**Authors:** Zijian Guo, İlker Işık, H. M. Sabbir Ahmad, Wenchao Li\n**Primary source:** https://arxiv.org/abs/2604.24729\n**Published:** 2026-04-27T17:40:18Z\n**Updated:** 2026-04-27T17:40:18Z\n**Categories:** cs.LG\n\n## Abstract Summary\nSpecification-guided reinforcement learning (RL) provides a principled framework for encoding complex, temporally extended tasks using formal specifications such as linear temporal logic (LTL). While recent methods have shown promising results, their ability to generalize across unseen specifications and diverse environments remains insufficiently understood. In this work, we introduce SpecRLBench, a benchmark designed to evaluate the generalization capabilities of LTL-based specification-guided RL methods. The benchmark spans multiple difficulty levels across navigation and manipulation domains, incorporating both static and dynamic environments, diverse robot dynamics, and varied observation modalities. Through extensive empirical evaluation, we characterize the strengths and limitations of existing approaches and reveal the challenges that emerge as specification and environment complexity increase. SpecRLBench provides a structured platform for systematic comparison and supports the development of more generalizable specification-guided RL methods. Code is available at https://github.com/BU-DEPEND-Lab/SpecRLBench.\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deployment-sensitive topics, treat the paper as research context rather than medical, legal, safety, or engineering advice.\n\n## Source\n- https://arxiv.org/abs/2604.24729","keywords":["cs.LG"],"about":[],"citation":["https://arxiv.org/abs/2604.24729"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-04-28T06:00:04.970000Z","dateModified":"2026-06-19T14:20:13Z","isBasedOn":"https://arxiv.org/abs/2604.24729","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":40},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"d95c89ac0a449de76acd2c9745f5f593731216bff10a9064cec5c0e9618d6621"}]}