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RAMBO's instability got you down? ROMI offers a robust, value-aware model learning approach with implicitly differentiable adaptive weighting that outperforms RAMBO and other SOTA methods in offline RL benchmarks.
By strategically resampling from deep, recoverable states ("pivots") within unsuccessful trajectories, DDE drastically improves LLM reinforcement learning compared to methods that oversample from the root or blindly disperse budgets.
