Natural Language Reinforcement Learning

2026-04-11

AI Summary

This work addresses the limitation of conventional reinforcement learning (RL) frameworks, which rely on discrete or continuous state-action spaces, by proposing Natural Language Reinforcement Learning (NLRL)—a novel paradigm that reformulates RL in natural language representation space. Methodologically, it systematically redefines core RL components—including task objectives, policies, value functions, the Bellman equation, and policy iteration—using linguistic symbols, thereby enabling language-based policy and value modeling and optimization; it supports both prompt-only engineering and gradient-based fine-tuning of large language models (LLMs). The primary contribution is the first formal, theoretically grounded NLRL framework, offering strong interpretability and cross-task generalization. Empirical evaluation on benchmark domains—including Maze, Breakthrough, and Tic-Tac-Toe—demonstrates the method’s effectiveness, training efficiency, and full traceability of decision-making processes via natural language.

Technology Category

Natural Language Processing: (Large) Language ModelsMachine Learning: Reinforcement LearningSearch and Optimization: Learning to Search

Application Category

Search and Retrieval-Augmented AI: Large language models for searchUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendationEconomics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Reinforcement Learning (RL) mathematically formulates decision-making with Markov Decision Process (MDP). With MDPs, researchers have achieved remarkable breakthroughs across various domains, including games, robotics, and language models. This paper seeks a new possibility, Natural Language Reinforcement Learning (NLRL), by extending traditional MDP to natural language-based representation space. Specifically, NLRL innovatively redefines RL principles, including task objectives, policy, value function, Bellman equation, and policy iteration, into their language counterparts. With recent advancements in large language models (LLMs), NLRL can be practically implemented to achieve RL-like policy and value improvement by either pure prompting or gradient-based training. Experiments over Maze, Breakthrough, and Tic-Tac-Toe games demonstrate the effectiveness, efficiency, and interpretability of the NLRL framework among diverse use cases.

Problem

• Extends MDP to natural language representation space
• Redefines RL principles into language counterparts
• Demonstrates NLRL effectiveness in diverse games

Innovation

• Extends MDP to natural language representation space
• Redefines RL principles into language counterparts
• Uses LLMs for policy and value improvement