Large language model (LLM) agents have demonstrated strong capabilities in complex interactive decision-making tasks.However, existing LLM agents typically rely on increasingly long interaction histories, resulting in high computational cost and limited scalability.In this paper, we propose STEP-HRL, a hierarchical reinforcement learning (HRL) framework that enables step-level learning by conditioning only on single-step transitions rather than full interaction histories.STEP-HRL structures tasks hierarchically, using completed subtasks to represent global progress of overall task.By introducing a local progress module, it also iteratively and selectively summarizes interaction history within each subtask to produce a compact summary of local progress.Together, these components yield augmented step-level transitions for both high-level and low-level policies.Experimental results on ScienceWorld and ALFWorld benchmarks consistently demonstrate that STEP-HRL substantially outperforms baselines in terms of performance and generalization while reducing token usage.

Electricity information tracking systems are increasingly being adopted across China. Such systems can collect real-time power consumption data from users, and provide opportunities for artificial intelligence (AI) to help power companies and authorities make optimal demand-side management decisions. In this paper, we discuss power utilization improvement in Shandong Province, China with a deployed AI application - the Power Intelligent Decision Support (PIDS) platform. Based on improved short-term power consumption gap prediction, PIDS uses an optimal power adjustment plan which enables fine-grained Demand Response (DR) and Orderly Power Utilization (OPU) recommendations to ensure stable operation while minimizing power disruptions and improving fair treatment of participating companies. Deployed in August 2018, the platform is helping over 400 companies optimize their power consumption through DR while dynamically managing the OPU process for around 10,000 companies. Compared to the previous system, power outage under PIDS through planned shutdown has been reduced from 16% to 0.56%, resulting in significant gains in economic activities.