Research
Thinking Deeper, Not Longer: Depth-Recurrent Transformers for Compositional Generalization [R]
This research paper explores a depth-recurrent transformer architecture designed to improve compositional generalization in language models by increasing computational depth rather than sequence lengt
This research paper explores a depth-recurrent transformer architecture designed to improve compositional generalization in language models by increasing computational depth rather than sequence length during inference. Instead of relying on extended chain-of-thought reasoning or longer token generation, the approach repeatedly applies transformer layers in a recurrent fashion over depth, enabling more structured and hierarchical reasoning. The work likely demonstrates improved performance on compositional generalization benchmarks, suggesting that deeper iterative processing is a more efficient path to systematic reasoning than simply generating more tokens.
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Source: r/MachineLearning | 2026-04-13