RISE: A Machine Learning Approach for Fine-Tuning LLMs
Enhancing Large Language Models’ Self-Improvement Capabilities
Large language models (LLMs) are powerful tools for various tasks, but face challenges when it comes to making decisions and improving their own responses. The RISE approach aims to address these challenges by enhancing LLMs’ self-improvement capabilities over multiple turns.
RISE employs an iterative fine-tuning procedure that frames single-turn prompts as multi-turn Markov decision processes. By incorporating principles from online imitation learning and reinforcement learning, RISE develops strategies for multi-turn data collection and training, enabling LLMs to recursively detect and correct mistakes in subsequent iterations.
RISE demonstrates significant performance improvements across multiple benchmarks, outperforming other methods and showing consistent gains in enhancing LLMs’ problem-solving abilities in complex scenarios.
If you want to evolve your company with AI, stay competitive, and leverage RISE to redefine your way of work. Connect with us for AI KPI management advice and continuous insights into leveraging AI.
