Expanding LLM Agent Boundaries with Strategy-Guided Exploration

• URL: http://arxiv.org/abs/2603.02045v1
• Date: Mon, 02 Mar 2026 16:28:39 GMT
• Title: Expanding LLM Agent Boundaries with Strategy-Guided Exploration
• Authors: Andrew Szot, Michael Kirchhof, Omar Attia, Alexander Toshev,
• Abstract summary: 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.<n>We propose Strategy-Guided Exploration (SGE) to shift exploration from low-level actions to higher-level language strategies.
• Score: 51.98616048282804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• MAGE: Meta-Reinforcement Learning for Language Agents toward Strategic Exploration and Exploitation [11.222602737031101]
  We propose MAGE, a meta-RL framework that empowers LLM agents for strategic exploration and exploitation.<n>MAGE utilizes a multi-episode training regime where interaction histories and reflections are integrated into the context window.<n>Experiment results show that MAGE outperforms existing baselines in both exploration and exploitation tasks.
  arXiv  Detail & Related papers  (2026-03-04T03:14:37Z)
• Guiding Exploration in Reinforcement Learning Through LLM-Augmented Observations [0.0]
  Large Language Models (LLMs) possess procedural knowledge and reasoning capabilities from text pretraining.<n>We propose a framework that provides LLM-generated action recommendations through augmented observation spaces.
  arXiv  Detail & Related papers  (2025-10-09T19:54:31Z)
• Reinforced Strategy Optimization for Conversational Recommender Systems via Network-of-Experts [63.412646471177645]
  We propose a novel Reinforced Strategy Optimization (RSO) method for Conversational Recommender Systems (CRSs)<n>RSO decomposes the process of generating strategy-driven response decisions into the macro-level strategy planning and micro-level strategy adaptation.<n>Experiments show that RSO significantly improves interaction performance compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2025-09-30T11:12:01Z)
• Emergent Hierarchical Reasoning in LLMs through Reinforcement Learning [56.496001894673235]
  Reinforcement Learning (RL) has proven highly effective at enhancing the complex reasoning abilities of Large Language Models (LLMs)<n>Our analysis reveals that puzzling phenomena like aha moments", length-scaling'' and entropy dynamics are not disparate occurrences but hallmarks of an emergent reasoning hierarchy.
  arXiv  Detail & Related papers  (2025-09-03T18:52:49Z)
• SAGE: Strategy-Adaptive Generation Engine for Query Rewriting [8.941793732446856]
  We introduce the Strategy-Adaptive Generation Engine (SAGE), which operationalizes expert-crafted strategies in an reinforcement learning framework.<n>SAGE achieves new state-of-the-art NDCG@10 results, but also uncovers a compelling emergent behavior.<n>Our findings demonstrate that strategy-guided RL, enhanced with nuanced reward shaping, offers a scalable, efficient, and more interpretable paradigm for developing the next generation of robust information retrieval systems.
  arXiv  Detail & Related papers  (2025-06-24T16:50:51Z)
• Strategy-Augmented Planning for Large Language Models via Opponent Exploitation [11.840105106884543]
  We introduce a two-stage Strategy-Augmented Planning (SAP) framework that significantly enhances the opponent exploitation capabilities of LLM-based agents.<n>In the offline stage, we construct an explicit strategy space and subsequently collect strategy-outcome pair data for training the Strategy Evaluation Network (SEN)<n>During the online phase, SAP dynamically recognizes the opponent's strategies and greedily exploits them by searching best response strategy on the well-trained SEN.
  arXiv  Detail & Related papers  (2025-05-13T11:41:10Z)
• EPO: Explicit Policy Optimization for Strategic Reasoning in LLMs via Reinforcement Learning [69.55982246413046]
  We propose explicit policy optimization (EPO) for strategic reasoning.<n>We train the strategic reasoning model via multi-turn reinforcement learning (RL),utilizing process rewards and iterative self-play.<n>Our findings reveal various collaborative reasoning mechanisms emergent in EPO and its effectiveness in generating novel strategies.
  arXiv  Detail & Related papers  (2025-02-18T03:15:55Z)
• WESE: Weak Exploration to Strong Exploitation for LLM Agents [95.6720931773781]
  This paper proposes a novel approach, Weak Exploration to Strong Exploitation (WESE) to enhance LLM agents in solving open-world interactive tasks. WESE involves decoupling the exploration and exploitation process, employing a cost-effective weak agent to perform exploration tasks for global knowledge. A knowledge graph-based strategy is then introduced to store the acquired knowledge and extract task-relevant knowledge, enhancing the stronger agent in success rate and efficiency for the exploitation task.
  arXiv  Detail & Related papers  (2024-04-11T03:31:54Z)
• LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models [75.89014602596673]
  Strategic reasoning requires understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. We explore the scopes, applications, methodologies, and evaluation metrics related to strategic reasoning with Large Language Models. It underscores the importance of strategic reasoning as a critical cognitive capability and offers insights into future research directions and potential improvements.
  arXiv  Detail & Related papers  (2024-04-01T16:50:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.