SGG-ReflAct: Sub-Goal Guided ReflAct with Structured Planning for Reliable Long-Horizon Reasoning

Recent advances in reasoning backbones have empowered large language model (LLM) agents to tackle complex, multi-step tasks. However, as reasoning horizons grow, inconsistent internal beliefs induce intermediate errors that cause agents to drift from their goals. This limitation also persists in REFLACT, which reflects only on the end-goal at each step without explicitly considering intermediate subgoals. To address this problem, we propose SGG-ReflAct (Sub-Goal Guided ReflAct), a reasoning backbone that integrates sub-goals generated through a singlepath LLM planner into the reflection process. We further extend this framework to BeamSGG-ReflAct, which replaces the single-path planner with a beam searchbased LLM planner for structured plan exploration. We run experiments on ALFWorld, ScienceWorld, and Jericho with multiple LLM models. SGG-ReflAct outperforms REFLACT in nearly all settings, achieving best success rate gains of 14.9 percentage points on ALFWorld and 8.0 percentage points on ScienceWorld with Llama-3.1-8B-Instruct. Our experimental analysis shows that SGG-ReflAct reduces hallucinated actions and achieves its largest gains on procedurally ordered tasks. Furthermore, experimental results with BeamSGG-ReflAct show that the backbone’s effectiveness depends on plan quality: explicitly specifying the required operations recovers gains that plan searching alone cannot achieve. These results demonstrate that SGG-ReflAct offers a practical and highly effective reasoning backbone, enabling LLM agents to achieve reliable performance in complex, long-horizon tasks through easy integration.