arXiv:2503. 08394v5 Announce Type: replace-cross Abstract: Multi-task optimization is typically characterized by a fixed and finite set of tasks.
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arXiv:2609.25836v1 Announce Type: new Abstract: Multi-task optimization (MTO) addresses a set of optimization tasks simultaneously, often suffering from inaccurate inter-task relationship estimation...
arXiv:2505. 15201v5 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) algorithms sample multiple n>1 solution attempts for each problem and reward them independently.
SIMS: Scale-Invariant Merit-Function-Based Scalarization for Multi-Task Learning proposes a new scalarization method for multi-task learning that is invariant to the relative scales of task losses. By using a logarithmic transformation, SIMS converts the multi-objective problem into a single objective that preserves weak Pareto optimality and allows a smooth surrogate with controllable approximation error. Experiments on standard multi-task benchmarks show that SIMS consistently outperforms existing scalarization methods and achieves state‑of‑the‑art performance.
arXiv:2606. 00366v1 Announce Type: new Abstract: We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems.
arXiv:2606. 02027v1 Announce Type: cross Abstract: Robot learning must produce policies that generalize to new combinations of constraints, teammates, and environments.
arXiv:2606. 25012v1 Announce Type: new Abstract: Many reinforcement learning (RL) problems in the infinite-horizon average-reward setting require optimizing multiple conflicting objectives while satisfying multiple safety constraints.