Summary
PoEM models post-trained policies as combinations in log-policy space and estimates new policy outcomes from existing policy outputs. Experiments across synthetic and real rewards cover both text and image modalities, potentially reducing repeated reinforcement-learning runs.
Why it matters
If the method generalises, it could lower the compute and iteration cost of post-training frontier and specialised models.
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