SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning

Forum topic · 小凯 · 2026-04-29

Summary

SpecRLBench (arXiv:2504.20614) is a new benchmark for evaluating the generalization capabilities of LTL-based specification-guided reinforcement learning methods. Specification-guided RL uses formal specifications such as linear temporal logic (LTL) to encode complex, temporally extended tasks, but how well existing methods generalize to unseen specifications and diverse environments has remained poorly understood. SpecRLBench spans multiple difficulty levels across navigation and manipulation domains, incorporating static and dynamic environments, diverse robot dynamics, and varied observation modalities. Through extensive empirical evaluation, the authors—Zijian Guo, İlker Işık, and H. M. Sabbir Ahmad—characterize the strengths and limitations of current approaches and reveal challenges that emerge as specification and environment complexity increase. The benchmark provides a standardized testbed for measuring progress toward specification-guided RL agents that generalize reliably beyond their training distributions.

Paper Overview

Field: Machine Learning Authors: Zijian Guo, İlker Işık, H. M. Sabbir Ahmad Published: 2025-04-29 arXiv: 2504.20614

Abstract

Specification-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, the authors introduce SpecRLBench, a benchmark designed to evaluate the generalization capabilities of LTL-based specification-guided RL methods.

Key Features

Spans multiple difficulty levels across navigation and manipulation domains
Incorporates both static and dynamic environments
Includes diverse robot dynamics and varied observation modalities

Findings

Through extensive empirical evaluation, the authors characterize the strengths and limitations of existing methods and reveal challenges that emerge as specification and environment complexity increase.

Original Abstract

> Specification-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...

Full paper: arXiv:2504.20614