TEMPO: recursive self-critique plus macro-step value estimation, not exploration rewards
teortaxesTex · x · 2026-08-15
Clarifying reader questions about TEMPO, the authors note:
- No direct exploration reward: TEMPO trains the model to self-critique its intermediate progress, then uses macro-step value estimation to turn those critiques into denser learning signals.
- Recursive self-critique matters most in open-ended environments, where an agent must repeatedly reassess progress, update strategy, and keep exploring without a predefined path. The authors also confirm RL is still ongoing.
teortaxesTex adds: a natural next step after PRMs, GRMs and self-play — but it took a good implementation and strong base models to actually fly.
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