The Goal Gets in the Way: Subgoal Relay for Long-Horizon LLM Agents

Language model agents often decompose long-horizon tasks into subtasks and use the final goal to organize and guide execution. Our motivation experiments reveal a contrast: using the final goal to clarify a subtask improves local completion, yet providing the goal again with the same explanation reduces completion. Direct goal guidance may lead the executor to reconsider earlier planning decisions, causing unproductive actions and rework. We therefore propose Subgoal Relay (SR) to limit the final goal's direct influence on local decisions while retaining its guidance for the current subtask. We aggregate goal information using attention weights from the current subgoal to the final goal. We fuse this information into the subgoal value cache and bound the update magnitude relative to the original values, forming a relay for local execution. The model draws on this relay and the retained context for its decisions, while attention access constraints block direct access to the original final-goal field. We evaluate SR on ALFWorld, ScienceWorld, and WebShop across four agent frameworks and six model backbones, measuring task performance and end-to-end execution cost. In the main experiments with Qwen3-8B under a shared attention backend, SR increases ALFWorld success rates by up to 7.8 percentage points and mean scores on ScienceWorld and WebShop by up to 4.6 and 8.7 points, respectively, while reducing mean end-to-end execution time by 2.5–11.1%.