Adding explicit strategy planning at the start of a task—rather than pure reactive decision-making—dramatically improves both learning efficiency and success rates for LLM agents on long-horizon tasks.
StraTA improves how language models learn to make decisions over many steps by having them first plan a high-level strategy before acting. Instead of reacting moment-by-moment, the model samples a strategy from the initial state, follows it through actions, and learns both strategy planning and action execution together.