LLM-RL-Papers

🥈Silver

Automates monitoring of arXiv for LLM and RL research focused on control applications. Benefits operations teams by identifying relevant papers and facilitating contributions via PRs. Connects to research workflows and documentation tools.

543350Updated Today

Intermediate30min to implementautomation

Saves ~15 min per use

Quick InstallView Source

git clone https://github.com/WindyLab/LLM-RL-Papers.git

Works with:

Claude

Overview

About This Skill

Use Cases

Automate the monitoring of new research papers on LLM and RL for timely updates.

Facilitate collaboration by allowing users to submit pull requests for noteworthy papers.

Enhance control systems in robotics by integrating insights from the latest research.

Support game development by applying findings from LLM and RL research to character behavior.

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/WindyLab/LLM-RL-Papers

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

Monitor arXiv for recent papers on LLM and RL research. Focus on papers from [COMPANY] or [INDUSTRY] experts. Summarize key findings and highlight any innovative control mechanisms. Provide a list of top 3 papers with titles, authors, and abstracts.

Example Output

# Recent LLM-RL Research on arXiv

## Top 3 Papers

### 1. **Title**: "Reinforcement Learning from Human Feedback for Language Models"
**Authors**: Jane Doe, John Smith
**Abstract**: This paper explores the integration of reinforcement learning from human feedback (RLHF) into large language models, demonstrating significant improvements in alignment and control.

### 2. **Title**: "Controlled Generation with Reinforcement Learning"
**Authors**: Alice Johnson, Bob Brown
**Abstract**: The authors propose a novel framework for controlled text generation using reinforcement learning, addressing issues of bias and toxicity.

### 3. **Title**: "Scalable Reinforcement Learning for Language Models"
**Authors**: Charlie Wilson, Diana Lee
**Abstract**: This study presents a scalable approach to reinforcement learning for language models, enabling efficient training on large-scale datasets.

## Key Findings
- RLHF improves alignment and control in language models.
- Novel frameworks address bias and toxicity in text generation.
- Scalable RL approaches enable efficient training on large datasets.