In controlled experiments, 2D positional encoding outperforms 1D, RoPE, and learned embeddings for transformer models on ARC-like tasks when training examples are limited.
Yue Wang, Hung Le, Akhilesh Gotmare, Nghi D
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The role of positional encodings in the ARC benchmark
In controlled experiments, 2D positional encoding outperforms 1D, RoPE, and learned embeddings for transformer models on ARC-like tasks when training examples are limited.