Installs into .claude/skills of the current project.
Are you the author of Jaxolotl A Unified High Performance Benchmark Suite?
Add the live security badge to your README. It updates with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-jaxolotl-a-unified-high-performance-benchmark-suit)
---
name: jaxolotl-a-unified-high-performance-benchmark-suite
description: Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
version: 1.0.0
tags: [cs.LG, cs.AI, multi-agent-rl]
source: arxiv
arxiv_id: 2609.38065v1
utility: 0.85
---
# Jaxolotl: A Unified High-Performance Benchmark Suite for LTL-Based Multi-Task RL
**Authors:** Mathias Jackermeier, Jacques Cloete, Alessandro Abate
**Published:** 2026-09-29
**Categories:** cs.LG, cs.AI
**arXiv:** https://arxiv.org/abs/2609.38065v1
## Summary
Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL). Linear temporal logic (LTL) provides a precise and structured formalism for specifying instructions to agents, and has been successfully adopted for training generalist multi-task policies. However, differences in implementations, task distributions, and evaluation protocols make existing methods difficult to compare, while high computational costs limit the scale and statistical reli
## Key Contributions
- Training agents to follow arbitrary instructions is an important goal of multi-task reinforcement learning (RL)
- Linear temporal logic (LTL) provides a precise and structured formalism for specifying instructions to agents, and has been successfully adopted for training generalist multi-task policies
- However, differences in implementations, task distributions, and evaluation protocols make existing methods difficult to compare, while high computational costs limit the scale and statistical reli
## Relevance
This paper addresses important challenges in multi-agent-rl research. With a utility score of 0.85, it provides valuable insights and methods that can be applied to related problems in the field. The work contributes to advancing understanding of jaxolotl.
