New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
Ruihan Yang, Huazhe Xu, Yi Wu, and Xiaolong Wang
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Adaptive multi-criteria scoring with online logistic regression for Benders subproblem selection yields statistically significant runtime and integral improvements on 135 survivable network design instances.
TOPPO reformulates PPO with critic balancing to address gradient ill-conditioning in multi-task RL and reports stronger mean and tail performance than SAC baselines on Meta-World+ using fewer parameters and steps.
citing papers explorer
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Model Merging: Foundations and Algorithms
New cycle-consistent optimization, task vector theory, singular vector decompositions, adaptive routing, and efficient evolutionary search provide foundations for merging neural network weights across tasks.
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Adaptive Subproblem Selection in Benders Decomposition for Survivable Network Design Problems
Adaptive multi-criteria scoring with online logistic regression for Benders subproblem selection yields statistically significant runtime and integral improvements on 135 survivable network design instances.
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TOPPO: Rethinking PPO for Multi-Task Reinforcement Learning with Critic Balancing
TOPPO reformulates PPO with critic balancing to address gradient ill-conditioning in multi-task RL and reports stronger mean and tail performance than SAC baselines on Meta-World+ using fewer parameters and steps.