by ZJU-REAL · Codex Skill · ★ 360
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SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization 🔥 Overview We introduce SKILL0, an in-context reinforcement learning framework designed for skill internalization. SKILL0 achieves substantial improvements over the standard RL baseline on ALFWorld and Search-QA. 🗞️ News : 🔥🔥 Our new work was released: SDAR, which introduces Self-Distilled Agentic Reinforcement Learning. : 🔥 Our new work was released: SKILL1, which evloves skill-augmented agents in one unified policy. : We release our paper and code. 🛠️ Installation Python environment bash conda c
| Stars | 360 |
| Forks | 16 |
| Language | Python |
| Category | Codex Skill |
| License | Apache-2.0 |
| Quality Score | 69.6022201272192/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-07 |
| Created | 2026-04-02 |
| Platforms | python |
| Est. Tokens | ~14k |
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SkillZero is Official code for "SKILL0: In-Context Agentic Reinforcement Learning for Skill Internalization". It is categorized as a Codex Skill with 360 GitHub stars.
SkillZero is primarily written in Python. It covers topics such as agent, curriculum-learning, in-context-reinforcement-learning.
You can find installation instructions and usage details in the SkillZero GitHub repository at github.com/ZJU-REAL/SkillZero. The project has 360 stars and 16 forks, indicating an active community.
SkillZero is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to SkillZero on Agent Skills Hub include milady, clawsec, OpenDerisk. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.