New Amazon Microsoft paper shows long-horizon agents should not need an LLM decision after every tiny action; the hard part is knowing which actions can safely run together.

SPACE learns those boundaries from successful trajectories.

It converts trajectories into programmatic skills, treats subskill boundaries as labels for meaningful chunks, then distills them into a policy that emits variable-length primitive actions with no skill library at test time.

On ScienceWorld, success rose from 35.9% to 67.2%, while average LLM rounds fell from 10.2 to 5.2.

do not make agents reconsider every tiny step, and do not blindly batch actions either. Train them to learn when to keep acting and when to look again.