NEW paper from Microsoft Research. Nice study on long-horizon agent generalization. (bookmark it) The team runs a study where the only varia…

NEW paper from Microsoft Research. Nice study on long-horizon agent generalization. (bookmark it) The team runs a study where the only variable is task horizon length. They use the same decision rules, reasoning structure but different sequence length to the goal. The main finding: horizon alone is a training bottleneck. As goal distance grows, exploration explodes combinatorially and credit assignment gets ambiguous. Models that learn cleanly on short horizons fall apart on long ones, even when the underlying reasoning is identical. But the fix is not more compute. It's horizon reduction. Re-parameterize the action space with macro actions that compress many low-level decisions into one. Training stabilizes immediately. They find that models trained on reduced horizons generalize to longer ones at inference time. The paper calls this horizon generalization. Why does it matter? Most teams treat long-horizon failures as a model capacity problem. This paper says it's a horizon problem. Reduce horizon during training, get stability now and generalization for free at inference. Paper: https://arxiv.org/abs/2605.02572 Learn to build effective AI agents in our academy: https://academy.dair.ai/

Source: DAIR.AI (X) | 2026-05-05