Google has just introduced Gemma, an innovative family of state-of-the-art open Large Language Models (LLMs), marking a significant stride in the open-source AI landscape. This release, featuring both 7B and 2B parameter models, underscores Google's ongoing commitment to open-source AI. The Hugging Face team is thrilled to support this launch, ensuring seamless integration within our ecosystem. …
A key challenge has been improving these models beyond a certain point, especially without the continuous infusion of human-annotated data. A groundbreaking paper by Zixiang Chen, Yihe Deng, Huizhuo Yuan, Kaixuan Ji, and Quanquan Gu presents an innovative solution: Self-Play Fine-Tuning (SPIN). …
Language models (LMs) have been making remarkable strides in understanding and generating human language. Yet, their true potential in problem-solving tasks has been somewhat limited by the reliance on human-generated data. The groundbreaking paper, "Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models", introduces a novel method named Reinforced Self-Training (ReST) that promises to change this landscape. …
In the dynamic world of Artificial Intelligence (AI), the realm of Reinforcement Learning (RL) has witnessed a paradigm shift, brought to the forefront by the groundbreaking paper "Deep Reinforcement Learning from Human Preferences". This novel approach, straying from the traditional pathways of predefined reward functions, paves the way for a more intuitive and human-centric method of training RL agents. Let's dive into the intricacies and implications of this innovative research. …
We've taken on the exciting challenge of implementing the cutting-edge strategies presented in "ZEPHYR: Direct Distillation of LM Alignment". This paper's approach is not just theoretical—it's a blueprint for a significant leap in language model training. By adopting ZEPHYR's distilled direct preference optimization (dDPO), we've embarked on a code journey that brings these innovations from concept to reality. …
In today's post, we delve into a recent paper that investigates the intricacies of Reinforcement Learning in the context of Large Language Models (LLMs). This study shines a light on the challenges and nuances of training such models to align better with human preferences. …
We've all been there - diligently using Proximal Policy Optimization (PPO) for text generation, only to wonder if there's more to be extracted from our models. If you've been in this boat, you're in for a treat! A recent paper under review for ICLR 2024 offers some intriguing insights. …
The paper proposes a new technique called "Constitutional AI" (CAI) to train AI systems like chatbots to be helpful, honest, and harmless without needing human feedback labels identifying harmful behaviors. Instead, the training relies entirely on AI-generated feedback guided by simple principles. This makes it possible to control AI behavior more precisely with far less human input. …