Introducing Reinforced Planning (RP-1) — Pantheon
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RP-1 matches or exceeds state-of-the-art latent methods on all 48 evaluation settings.
150 days after inception, we’re excited to announce a major breakthrough towards solving robot learning: Reinforced Planning (RP-1), a novel method that uses Reinforcement Learning to improve World Model planning. Robot learning today runs either on Imitation Learning methods, which cannot reason over counterfactuals, or on sampling-based search, which plans too slowly for real-world deployment. RP-1 offers a third option: by reimagining planning as a learned policy over a frozen world model, it achieves higher success rates at a fraction of the compute and time spent. How does Reinforced…
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