RLHF - Labellerr

RLHF

A collection of 4 posts

DPO vs PPO: Aligning Large Language Models with Human Preferences

DPO vs PPO: How To Align LLM

Direct Preference Optimization (DPO) and Proximal Policy Optimization (PPO) are two approaches to align Large Language Models with human preferences. DPO focuses on human feedback to optimize models directly, while PPO uses reinforcement learning for iterative improvements.

2 Best RLHF Libraries in 2024

Best RLHF Libraries in 2024

Imagine you're training a computer to understand and generate human-like language – that's what RLHF helps us do! It's like teaching a computer to get better at talking, writing, and understanding language by giving it feedback. RLHF is important because it helps us make those

9 Top Tools and Libraries for RLHF in 2024

7 Top Tools for RLHF in 2024

Reinforcement Learning from Human Feedback (RLHF) is a technique used in machine learning, specifically in the training of models to incorporate human input and feedback throughout the learning process. This approach is particularly beneficial for Large Language Models (LLMs) that may be challenging to train using traditional supervised learning methods.

TRLx: Hands-on Guide for Implementing Text Summarization through RLHF

Exploring TRLx: Hands-on Guide for Implementing Text Summarization through RLHF

This guide provides a hands-on approach to implementing a text summarization tool utilizing the Reinforcement Learning from Human Feedback (RLHF) method. OpenAI researchers, in their paper, 'Learning to Summarize from Human Feedback' (Stiennon et al., 2020), applied RLHF to GPT model. This blog will explore the implementation of