Expanding LLM Agent Boundaries with Strategy-Guided Exploration

2026-03-02

AI Summary

This work addresses the challenge of inefficient exploration in large language model (LLM) agents within reinforcement learning, where vast action spaces and sparse rewards hinder effective learning. To overcome this, the authors propose the Strategy-Guided Exploration (SGE) framework, which elevates exploration from the low-level action space to a high-level natural language strategy space. SGE integrates natural language strategy generation, strategy-conditioned action execution, mixed-temperature sampling, and a strategy refinement mechanism grounded in environmental feedback, thereby enhancing both the structure and diversity of exploration. Experimental results demonstrate that SGE significantly outperforms existing reinforcement learning baselines across diverse domains—including UI interaction, tool use, code generation, and embodied tasks—improving both sample efficiency and final performance while successfully solving complex tasks beyond the capability of base models.

Technology Category

• Machine Learning: Reinforcement Learning
• Search and Optimization: Learning to Search
• Natural Language Processing: (Large) Language Models

Application Category

• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions

Abstract

Reinforcement learning (RL) has demonstrated notable success in post-training large language models (LLMs) as agents for tasks such as computer use, tool calling, and coding. However, exploration remains a central challenge in RL for LLM agents, especially as they operate in language-action spaces with complex observations and sparse outcome rewards. In this work, we address exploration for LLM agents by leveraging the ability of LLMs to plan and reason in language about the environment to shift exploration from low-level actions to higher-level language strategies. We thus propose Strategy-Guided Exploration (SGE), which first generates a concise natural-language strategy that describes what to do to make progress toward the goal, and then generates environment actions conditioned on that strategy. By exploring in the space of strategies rather than the space of actions, SGE induces structured and diverse exploration that targets different environment outcomes. To increase strategy diversity during RL, SGE introduces mixed-temperature sampling, which explores diverse strategies in parallel, along with a strategy reflection process that grounds strategy generation on the outcomes of previous strategies in the environment. Across UI interaction, tool-calling, coding, and embodied agent environments, SGE consistently outperforms exploration-focused RL baselines, improving both learning efficiency and final performance. We show that SGE enables the agent to learn to solve tasks too difficult for the base model.

Problem

• LLM agents
• reinforcement learning
• exploration
• language-action spaces
• sparse rewards

Innovation

• Strategy-Guided Exploration
• Large Language Model Agents
• Reinforcement Learning
• Language-based Planning
• Exploration in RL