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

ACL ARR 2026 January Submission4299 Authors

Keywords: Large Language Model, Agent, Reinforcement Learning

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. Build upon this paradigm, we further propose Diverse Parallel Exploration Policy Optimization (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.

Paper Type: Long

Research Area: AI/LLM Agents

Research Area Keywords: LLM/AI agents, Reinforcement Learning

Contribution Types: Model analysis & interpretability

Languages Studied: English

Submission Number: 4299