Graph-based RL agents can solve logic puzzles larger than anything seen in training, with graph structure, reward design, and recurrence each changing how far extrapolation goes.
Triplet Edge Attention for Algorithmic Reasoning
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abstract
This work investigates neural algorithmic reasoning to develop neural networks capable of learning from classical algorithms. The main challenge is to develop graph neural networks that are expressive enough to predict the given algorithm outputs while generalizing well to out-of-distribution data. In this work, we introduce a new graph neural network layer called Triplet Edge Attention (TEA), an edge-aware graph attention layer. Our algorithm works by precisely computing edge latent, aggregating multiple triplet messages using edge-based attention. We empirically validate our TEA layer in the CLRS benchmark and demonstrate a $5%$ improvement on average. In particular, we achieve a $30%$ improvement for the string algorithms compared to the state-of-the-art model.
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cs.LG 1years
2025 1verdicts
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Beyond Interpolation: Extrapolative Reasoning with Reinforcement Learning and Graph Neural Networks
Graph-based RL agents can solve logic puzzles larger than anything seen in training, with graph structure, reward design, and recurrence each changing how far extrapolation goes.