Stochastic loop counts during training of looped transformers reduce OOD variance on binary addition, Dyck-1, Unique Set and Copy tasks, with learned RL-Halting further improving the accuracy-stability trade-off.
Ex- trapolation by association: Length generalization transfer in transformers.arXiv preprint arXiv:2506.09251
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LLMs show strong spatial generalization to unseen maps in shortest-path tasks but fail length scaling due to recursive instability, with data coverage setting hard limits.
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Stabilizing Extrapolation in Looped Transformers via Learned Stochastic Stopping
Stochastic loop counts during training of looped transformers reduce OOD variance on binary addition, Dyck-1, Unique Set and Copy tasks, with learned RL-Halting further improving the accuracy-stability trade-off.
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Generalization in LLM Problem Solving: The Case of the Shortest Path
LLMs show strong spatial generalization to unseen maps in shortest-path tasks but fail length scaling due to recursive instability, with data coverage setting hard limits.