Tag

#human-feedback

Every item tagged human-feedback, newest first.

2 items

Putting RL back in RLHF

Researchers propose RLOOP, a modification to the popular RLHF framework that incorporates reinforcement learning from human feedback. RLOOP aims to improve model performance by leveraging human feedback more effectively. The approach has shown promising results in preliminary experiments. You can explore the RLOOP implementation on the Hugging Face platform.

Key takeaways

RLOOP modifies RLHF to better leverage human feedback.
Preliminary experiments show promising results.
Implementation available on Hugging Face platform.

HHugging Face Blog#reinforcement-learning #rlhf #human-feedback

researchDec 9

Illustrating Reinforcement Learning from Human Feedback (RLHF)

The Hugging Face blog post explains Reinforcement Learning from Human Feedback (RLHF), a technique for training AI models to align with human preferences. RLHF involves collecting human feedback, training a reward model, and fine-tuning the AI model. This approach enables builders to create more accurate and relevant models.

Key takeaways

RLHF involves collecting human feedback to train AI models.
A reward model is trained to predict human preferences.
The AI model is fine-tuned based on the reward model.

HHugging Face Blog#reinforcement-learning #human-feedback #fine-tuning