steppo-agentic-rl - SKILL.md Agent Skill

name: steppo-agentic-rl description: 'StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning - A novel RL framework for training LLM agents with step-level credit assignment' metadata: openclaw: emoji: "🤖" tags: ["research", "arxiv", "agentic-rl", "reinforcement-learning", "llm-agents"]

StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning

Paper: StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning
arXiv ID: 2604.18401v1
Published: 2026-04-20
Categories: cs.CL
Utility Score: 0.91
URL: http://arxiv.org/abs/2604.18401v1

Authors

Daoyu Wang, Qingchuan Li, Mingyue Cheng, Jie Ouyang, Shuo Yu

Abstract

General agents have given rise to phenomenal applications such as OpenClaw and Claude Code. As these agent systems (a.k.a. Harnesses) strive for bolder goals, they demand increasingly stronger agentic capabilities from foundation Large Language Models (LLMs). Agentic Reinforcement Learning (RL) is emerging as a central post-training paradigm for empowering LLMs with these capabilities and is playing a vital role in the agent ecosystem.

Key Contributions

Step-Aligned Policy Optimization (StepPO): A novel RL framework specifically designed for agentic tasks
Step-level Credit Assignment: Addresses the challenge of credit assignment in multi-step agent trajectories
Foundation for Agent Systems: Provides training methodology for next-generation agent harnesses like OpenClaw and Claude Code

Relevance to AI Agent Systems

This paper is highly relevant to the development of autonomous AI agents:

Agentic RL Paradigm: Establishes RL as a central post-training method for LLM agents
Tool Use & Reasoning: Enhances capabilities critical for tool-augmented agents
Production Systems: Directly applicable to real-world agent deployments

Technical Keywords

Agentic Reinforcement Learning
Step-level Credit Assignment
LLM Post-Training
Tool-Augmented Agents
Multi-step Reasoning

Citation

@article{wang2026steppo,
  title={StepPO: Step-Aligned Policy Optimization for Agentic Reinforcement Learning},
  author={Wang, Daoyu and Li, Qingchuan and Cheng, Mingyue and Ouyang, Jie and Yu, Shuo},
  journal={arXiv preprint arXiv:2604.18401},
  year={2026}
}

Discovered: 2026-04-21
Source: arXiv Paper Tracker (Daily Cron Job)