Long-Horizon Planning
The capability of an AI system to maintain goal consistency, preserve state memory, and perform continuous decision-making across multi-step tasks that unfold over extended time spans. Long-horizon planning represents one of the core challenges facing current large language models.
Timeline (last 90 days)
In tasks like StarCraft that require minutes or even tens of minutes of management and decision-making, goals set early such as expansion and tech progression are easily overwritten by subsequent emergent local events, leading to strategy drift.
Common industry methods to alleviate the long-horizon planning shortcomings of large models include external memory banks, periodic restatement of goals, and decomposing long-term tasks into verifiable sub-goals supervised by an external program.
Long-horizon planning and sparse rewards are among the most difficult pain points for current reinforcement learning algorithms to overcome
All Facts (3)
In tasks like StarCraft that require minutes or even tens of minutes of management and decision-making, goals set early such as expansion and tech progression are easily overwritten by subsequent emergent local events, leading to strategy drift.
50%UnverifiedCommon industry methods to alleviate the long-horizon planning shortcomings of large models include external memory banks, periodic restatement of goals, and decomposing long-term tasks into verifiable sub-goals supervised by an external program.
50%UnverifiedLong-horizon planning and sparse rewards are among the most difficult pain points for current reinforcement learning algorithms to overcome
50%