让大模型分步规划、小模型执行，自动组合技能完成复杂任务

We study goal-conditioned reinforcement learning in partially observable environments with sparse rewards and large, structured goal spaces. In such settings, complex goals often require composing simpler skills, but learning these compositions efficiently remains challenging. This difficulty is particularly relevant in open-ended exploration settings, where agents are exposed to increasingly complex goals over time and must continuously expand and reorganize their skill repertoire. We assume the goal space admits prerequisite relations, enabling latent decomposition of tasks into subgoals, and leverage language to represent and reason over these goals. To exploit this structure, we propose HERAKLES, a hierarchical agent that jointly learns a high-level LLM policy and a low-level controller. The high-level policy selects subgoals among those the low-level can reliably achieve, while the low-level executes them and progressively compiles successful behaviors into reusable skills. Both policies are trained concurrently: the high-level guides exploration and structures behavior, while the low-level distills trajectories into efficient goal-conditioned skills. As training progresses, more goals become directly executable, enabling scalable skill composition. This results in a hybrid system combining planning and execution, improving efficiency and adaptation in open-ended, compositional environments.