awesome-rlhf
github.com/opendilab/awesome-rlhf ↗A curated list of reinforcement learning with human feedback resources (continually updated)
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Curated Resources
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me 2023 resources from awesome-rlhf"
Installation instructions →What's inside
Papers
- A Baseline Analysis of Reward Models' Ability To Accurately Analyze Foundation Models Under Distribution Shift2023
- ActiveDPO: Active Direct Preference Optimization for Sample-Efficient Alignment2026
- ActiveUltraFeedback: Efficient Preference Data Generation using Active Learning2026
- A Dense Reward View on Aligning Text-to-Image Diffusion with Preference2024
- Adversarial Preference Optimization2023
- A General Theoretical Paradigm to Understand Learning from Human Preferences2023
Blogs
- A Survey of Reinforcement Learning from Human Feedback (RLHF)
- ChatGPT: Optimizing Language Models for Dialogue
- Guide to Reinforcement Learning from Human Feedback (RLHF) for Computer Vision
- Illustrating Reinforcement Learning from Human Feedback (RLHF)
- John Schulman - Reinforcement Learning from Human Feedback: Progress and Challenges
Reinforcement Learning from Human Feedback: Progress and Challenges
- Learning through human feedback
Showing a sample of 360 resources. View the full list on GitHub →