Banger paper from Microsoft and colleagues. We talk about human taste being important in this AI era. But for recursive self-improvement, an…

Banger paper from Microsoft and colleagues. We talk about human taste being important in this AI era. But for recursive self-improvement, an agent's taste also matters. The big question is: How often do frontier agents pick the better direction at a decision point in a long task? It's apparently just under 60% of the time, according to Taste-Bench. In this benchmark, each question shows a point in a trajectory where several directions are open, and one leads to a better outcome. The forks are mined automatically from parallel attempts and detours in engineering and research runs. The best model answers 59.7% correctly. Forks whose deciding evidence appears later in the trajectory are much harder, and a larger reasoning budget does not raise accuracy. The authors then distill a teacher's judgment of the outcome into a student model, which improves end-to-end success on held-out SWE-bench Pro tasks. Paper: https://arxiv.org/abs/2609.25804 Chat with Paper: https://academy.dair.ai/papers/the-tasteful-agent-measuring-and-improving-taste-in-long-horizon-tasks-2609.25804

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Source: DAIR.AI (X) | 2026-09-25