No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by xhyumiracle · Agent Tool · ★ 1.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Awesome-AgenticLLM-RL-Papers safe to install? View the security audit →
Awesome-AgenticLLM-RL-Papers This is the Official repo for the survey paper: The Landscape of Agentic Reinforcement Learning for LLMs: A Survey ArXiv – https://arxiv.org/abs/2509.02547 HuggingFace – https://huggingface.co/papers/2509.02547 Citation Sec2.7 Agentic RL: Algorithms Clip corresponds to preventing the policy ratio from moving too far from 1 for ensuring stable updates. KL penalty corresponds to penalizing the KL divergence between the learned policy and the reference policy for ensuring alignment.
| Stars | 1,816 |
| Forks | 79 |
| Category | Agent Tool |
| Quality Score | 42.3043701318007/100 |
| Open Issues | 5 |
| Last Updated | 2026-06-18 |
| Created | 2025-08-29 |
| Est. Tokens | ~14k |
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Awesome-AgenticLLM-RL-Papers is an open-source agent tool by xhyumiracle with 1.8k GitHub stars.
You can find installation instructions and usage details in the Awesome-AgenticLLM-RL-Papers GitHub repository at github.com/xhyumiracle/Awesome-AgenticLLM-RL-Papers. The project has 1.8k stars and 79 forks, indicating an active community.
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: