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This work reframe agentic trajectories as paths through a shared embedding space of actions and environmental observations, and introduces Precog-RM, a transformer-based model that meta-learns a representation in which outcome prediction can be performed in a few-shot setting.
The results indicate that imperfect verification can support effective RLVR in the tested settings, while aggregate error rates alone do not characterize the learning signal.
This paper compares two low-cost choices retrospectively -- a cost-reducing choice and a balanced choice -- with estimated grading cost reductions and average post-training score gaps of 1-3 points from the best evaluated training verifier, and establishes that verifier choices are interchangeable.
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