Agents Learn Their Runtime: Interpreter Persistence as Training-Time Semantics

Researchers found that AI agents perform better when their training data matches their deployment environment, specifically regarding interpreter state persistence. Models trained with persistent state but deployed in stateless environments trigger errors in 80% of cases, while the reverse wastes 3.5x more tokens through redundant computations.

• →Training AI agents with execution semantics that match deployment environments significantly improves efficiency and reduces errors.
• →Misaligned training leads to either frequent missing-variable errors (80% of episodes) or excessive token usage (3.5x increase).
• →Solution quality remains consistent across different training approaches, but computational efficiency varies dramatically.
• →Interpreter state persistence should be considered a core semantic feature during agent training, not just an inference-time tool.
• →The research introduces 'Opaque Knapsack' tasks to systematically study multi-turn agent behavior and state management.