Comparing various RLHF methods
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Updated
Sep 23, 2024 - Jupyter Notebook
Comparing various RLHF methods
Code for Bachelor thesis, The Human Factor: Addressing Diversity in Reinforcement Learning from Human Feedback.
Summaries of papers related to the alignment problem in NLP
This repository contains the implementation of a Reinforcement Learning with Human Feedback (RLHF) system using custom datasets. The project utilizes the trlX library for training a preference model that integrates human feedback directly into the optimization of language models.
LMRax is a framework built on JAX to train transformers language models by reinforcement learning, along with the reward model training.
[TSMC] Ask-AC: An Initiative Advisor-in-the-Loop Actor-Critic Framework
RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback
Shaping Language Models with Cognitive Insights
annotated tutorial of the huggingface TRL repo for reinforcement learning from human feedback connecting equations from PPO and GAE to the lines of code in the pytorch implementation
A repo for RLHF training and BoN over LLMs, with support for reward model ensembles.
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback
Super-Efficient RLHF Training of LLMs with Parameter Reallocation
A simulation framework for RLHF and alternatives. Develop your RLHF method without collecting human data.
Safe RLHF: Constrained Value Alignment via Safe Reinforcement Learning from Human Feedback
An Easy-to-use, Scalable and High-performance RLHF Framework (70B+ PPO Full Tuning & Iterative DPO & LoRA & Mixtral)
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