Turn-level hindsight feedback with cross-horizon validation provides denser, more reliable training signals for tool-using AI agents than traditional trajectory-level approaches, improving learning efficiency in long-horizon reasoning tasks.
TurnSight improves how language models learn to use tools by providing better feedback during training. Instead of judging entire sequences at once, it gives turn-by-turn (step-by-step) feedback based on what actually happened, using multiple future perspectives to decide which feedback is most reliable. This helps models learn faster and more accurately in complex multi-step tasks.