Google DeepMind has introduced Weight Averaged Rewarded Policies (WARP), a new method for Reinforcement Learning with Human Feedback (RLHF) alignment. WARP uses iterative model merging techniques to enhance performance and avoid reward hacking. By integrating exponential moving average anchor in KL regularization, spherical interpolation of policy weights, and linear interpolation towards the initialization, WARP significantly improves the performance of the Gemma LLM, surpassing previous releases. This innovative approach scales alignment similarly to pre-training, making it a state-of-the-art method in the field. The findings are detailed in a Gemma-based research paper.
Google presents WARP: On the Benefits of Weight Averaged Rewarded Policies - Merges policies in the weight space at three distinct stages - Gemma policies w/ WARP outperforms other open-source LLMs https://t.co/XDIL8GfQbQ https://t.co/NHuyhN4u6A
WARP is our new LLM alignment strategy based on iterative model merging through 1) exponential moving average anchor in KL regularization, 2) spherical interpolation of policy weights and 3) linear interpolation towards the init (+ repeat!) ✨ Paper: https://t.co/kK7wZlbVrb https://t.co/FFZM4Pku4F
Check out our latest Gemma-based research paper: WARP is an effective method to improve the performance of your RLHF loop via iterative model merging https://t.co/AyRguSRZdz
The magic of model merging strikes again! ✨ Iterative model merging between different models during RLHF, greatly improves performance while avoiding excessive reward hacking. Awesome work led by @ramealexandre 👏 https://t.co/WicNBkGs39
Introducing Weight Averaged Rewarded Policies (WARP), Google DeepMind's latest RLHF alignment method using the magic of model merging. By scaling alignment like pre-training was scaled, WARP learns sota Gemma LLM surpassing previous releases. A 🧵below. https://t.co/Ck2VWNQKBA
Fresh on arXiv, our new paper: "Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework." by Zackary Rackauckas, @ArthurCamara and @jakubzavrel. https://t.co/lH7XsztxZZ More and more, we are seeing the use of strong LLMs to annotate search engine and RAG outputs… https://t.co/N7jy24gwzX