Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.

A continually updated list of literature on Reinforcement Learning from AI Feedback (RLAIF)

ZYN: Zero-Shot Reward Models with Yes-No Questions

code for paper Query-Dependent Prompt Evaluation and Optimization with Offline Inverse Reinforcement Learning

Code and data for "Timo: Towards Better Temporal Reasoning for Language Models" (COLM 2024)

distilled Self-Critique refines the outputs of a LLM with only synthetic data

A curated and updated list of relevant articles and repositories on Reinforcement Learning from AI Feedback (RLAIF)