DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents

• URL: http://arxiv.org/abs/2604.24320v1
• Date: Mon, 27 Apr 2026 11:09:49 GMT
• Title: DPEPO: Diverse Parallel Exploration Policy Optimization for LLM-based Agents
• Abstract summary: Large language model (LLM) agents that follow the sequential "reason-then-act" paradigm have achieved superior performance in many complex tasks.<n>We introduce a novel paradigm that enables an agent to interact with multiple environments simultaneously.<n>We propose DPEPO, a reinforcement learning algorithm that encourages the agent to perform diverse parallel exploration.
• Score: 38.16347415282427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language model (LLM) agents that follow the sequential "reason-then-act" paradigm have achieved superior performance in many complex tasks.However, these methods suffer from limited exploration and incomplete environmental understanding, as they interact with only a single environment per step. In this paper, we first introduce a novel paradigm that enables an agent to interact with multiple environments simultaneously and share cross-trajectory experiences. Building upon this paradigm, we further propose DPEPO, a reinforcement learning (RL) algorithm that encourages the agent to perform diverse parallel exploration. There are two stages in DPEPO: initial supervised fine-tuning (SFT) imparts basic parallel reasoning and action generation, followed by reinforcement learning stage with a hierarchical reward scheme. We design a parallel trajectory-level success reward and two step-level rewards: Diverse Action Reward and Diverse State Transition Reward, which actively penalize behavioral redundancy and promote broad exploration. Extensive experiments on ALFWorld and ScienceWorld show that DPEPO achieves state-of-the-art (SOTA) success rates, while maintaining comparable efficiency to strong sequential baselines. (Code is available at https://github.com/LePanda026/Code-for-DPEPO)

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