How do you train an agent when a single rollout takes tens of hours? Alongside the open-source release of dots3-note Preview, we’re sharing more details about TEMPO: Test-Time-Scaled Value Estimation with Macro-Step Policy Optimization. Long-horizon agent RL faces two fundamental problems: • Training signals arrive only when an extremely long trajectory ends • Final rewards make it difficult to identify which intermediate decisions actually mattered TEMPO breaks a long trajectory into macro-steps. At each boundary, the same model switches from actor to generative critic: it reviews the interaction history, checks the agent’s hypotheses, reasons about possible futures, and estimates the remaining return. The agent therefore learns not only how to act, but also how to evaluate itself. One result surprised us: two branches can receive exactly the same environment reward, while one is trapped by an incorrect understanding of the task and the other has already found a viable direction. A reasoning critic can distinguish these states before the task ends. On the public ARC-AGI-3 set, TEMPO improves the average Score by 31.6% over the base checkpoint and 20.7% over GRPO, while reaching comparable progress with fewer environment interactions. TEMPO is still early work, but we believe test-time-scaled self-evaluation may be an important ingredient for training agents on tasks that last hours, days, or eventually much longer. Technical blog: studio.dots.ai/dots/tempo-bl… Open-source dots3-note Preview: studio.dots.ai/dots/dots3-en…

Dots3-Note Preview is free available for all mercury agent users. Use it now: cloud.mercuryagent.sh