TEMPO: Solving Long-Horizon Agent Training via Macro-Step Policy Optimization
teortaxesTex · x · 2026-08-15
To address sparse training signals and credit assignment challenges in long-horizon agent RL, researchers introduced TEMPO (Test-Time-Scaled Value Estimation with Macro-Step Policy Optimization). This method breaks long trajectories into macro-steps. At each boundary, the model switches roles from actor to generative critic, reviewing history, validating hypotheses, and estimating remaining return. This enables the agent to learn both acting and self-evaluation. The technique was released alongside the open-source dots3-note Preview.
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