A Subgoal-driven Framework for Improving Long-Horizon LLM Agents
Large language model (LLM)-based agents have emerged as powerful autonomous controllers for digital environments, including mobile interfaces, operating systems, and web browsers. Web navigation, for example, requires handling dynamic content and long sequences of actions, making it particularly challenging. Existing LLM-based agents struggle with long-horizon planning in two main ways. During online execution, they often lose track as new information arrives, lacking a clear and adaptive path toward the final goal. This issue is further exacerbated during reinforcement learning (RL) fine-tuning, where sparse and delayed rewards make it difficult for agents to identify which actions lead ...
| Authors | Taiyi Wang, Sian Gooding, Florian Hartmann et al. |
|---|---|
| Primary category | cs.AI |
| Published | 20 March 2026 |
| Last updated at source | 20 March 2026 |
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