NLI autonomously discovers a vocabulary of primitive operations and interprets variable-length programs via a neural executor, allowing end-to-end training and gradient-based test-time adaptation that outperforms prior methods on combinatorial generalization tasks.
arXiv preprint arXiv:2106.05126 , year=
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NICO-TSP learns a 2-opt local search policy for TSP using edge tokens, imitation learning on short trajectories, and critic-free group RL on longer rollouts, yielding more step-efficient improvement and better out-of-distribution generalization than prior neural and heuristic baselines.
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Gradient-Based Program Synthesis with Neurally Interpreted Languages
NLI autonomously discovers a vocabulary of primitive operations and interprets variable-length programs via a neural executor, allowing end-to-end training and gradient-based test-time adaptation that outperforms prior methods on combinatorial generalization tasks.
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A First Guess is Rarely the Final Answer: Learning to Search in the Traveling Salesperson Problem
NICO-TSP learns a 2-opt local search policy for TSP using edge tokens, imitation learning on short trajectories, and critic-free group RL on longer rollouts, yielding more step-efficient improvement and better out-of-distribution generalization than prior neural and heuristic baselines.