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Purdue University, Waymo LLC
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Curvature-aware model merging, previously intractable, can now be efficiently approximated in a low-rank subspace, provably outperforming flat-geometry methods and unifying prior spectral techniques.
By recasting the Hamilton-Jacobi-Bellman equation as a tractable Monte Carlo estimation, this work stabilizes physics-informed RL and unlocks its potential for high-dimensional control tasks.
