COALA applies convex optimization reformulations of neural networks to direct preference optimization, claiming single-GPU training with ~18% of DPO's TFLOPs and competitive performance on multiple datasets and models up to 8B parameters.
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Optimizing trajectory-trees in belief space improves performance in partially observable robotic planning by capturing observation-dependent contingencies, shown via PO-MPC with D-AuLa optimization and PO-LGP extending LGP.
PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.
citing papers explorer
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Convex Optimization for Alignment and Preference Learning on a Single GPU
COALA applies convex optimization reformulations of neural networks to direct preference optimization, claiming single-GPU training with ~18% of DPO's TFLOPs and competitive performance on multiple datasets and models up to 8B parameters.
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Optimizing Trajectory-Trees in Belief Space: An Application from Model Predictive Control to Task and Motion Planning
Optimizing trajectory-trees in belief space improves performance in partially observable robotic planning by capturing observation-dependent contingencies, shown via PO-MPC with D-AuLa optimization and PO-LGP extending LGP.
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Decision-Focused Learning via Tangent-Space Projection of Prediction Error
PEAR computes regret gradients via tangent-space projection of prediction error, delivering top decision quality and efficiency on LP and QP tasks without solver differentiation.