This Paper from Google DeepMind Presents Conditioned Language Policies (CLP): A Machine Learning Framework for Finetuning Language Models on Multiple Objectives

Reinforcement Learning for Language Models Practical Solutions and Value Multi-Objective Finetuning (MOFT) MOFT is essential for training language models (LMs) to behave in specific ways and follow human etiquette. It overcomes the limitations of single-objective finetuning (SOFT) by enabling LMs to adapt to various human preferences and usage. Approaches to MOFT Two main techniques for multi-reward alignment are prompt-based and parameter-based conditioning. Prompt-based methods involve customized prompts to personalize LMs based on reward weightings, while parameter-based methods use parameter-space conditioning and multi-task training. Conditional Language Policy (CLP) Google’s CLP framework is adaptable and produces superior responses compared to existing baselines. It offers a flexible approach for finetuning LMs on multiple objectives, creating adaptable models that can efficiently balance different individual rewards. AI Implementation Identify Automation Opportunities, Define KPIs, Select an AI Solution, and Implement Gradually. AI KPI Management Advice Connect with us at hello@itinai.com for AI KPI management advice. Continuous Insights Stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom for continuous insights into leveraging AI. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom