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 anakin87 · LLM Plugin · ★ 218
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LLM RL Environments Lil Course A little course on Reinforcement Learning Environments for evaluating and training Language Models. Unlike classic fine-tuning, RL environments let models explore and improve beyond what curated datasets can teach. In this course, we'll build a Tic Tac Toe environment and use it to transform a Small Language Model () into a master player that beats . ➡️ Start here: Chapter 1 - Agents, Environments, and LLMs 🎥 Video walkthrough @ AI Engineer 🤗🕹️ Play against Mr. Tic Tac Toe Who is this course for? AI Engineers: You are familiar with classic LLM fine-tuning techniques (Supervised Fine-Tuning) but have little to no experience with Reinforcement Learning. Traditional RL Practitioners: You know how RL works, but you want to learn how to apply it to Language Models. Curious Tinkerers: You keep hearing about "reasoning models" and RL post-training, and you want to see how it works under the hood. Chapters ➡️ Start here: Chapter 1 - Agents, Environments, and LLMs Agents, Environments, and LLMs: mapping Reinforcement Lear
| Stars | 218 |
| Forks | 17 |
| Language | Python |
| Category | LLM Plugin |
| License | Apache-2.0 |
| Quality Score | 55.7845155690582/100 |
| Last Updated | 2026-05-27 |
| Created | 2026-01-18 |
| Platforms | python |
| Est. Tokens | ~1994k |
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llm-rl-environments-lil-course is 🌱 A little course on Reinforcement Learning Environments for evaluating and training Language Models. It is categorized as a LLM Plugin with 218 GitHub stars.
llm-rl-environments-lil-course is primarily written in Python. It covers topics such as course, grpo, language-models.
You can find installation instructions and usage details in the llm-rl-environments-lil-course GitHub repository at github.com/anakin87/llm-rl-environments-lil-course. The project has 218 stars and 17 forks, indicating an active community.
llm-rl-environments-lil-course is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to llm-rl-environments-lil-course on Agent Skills Hub include hands-on-llm, Open-AgentRL, Awesome-LLM-Papers-Comprehensive-Topics. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
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.
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