Training Crossroads for Recurrent Vision Transformers: Recurrence, Neural ODEs, and Deep Supervision

arXiv:2608.04879 · cs.LG, cs.CV · Submitted 2026-08-05 · Read on arXiv

Grzegorz Gruszczynski, Pawel Olszowiec, Michal Byra, Grzegorz Stefanski, Alberto Presta

cs.LG, cs.CV

Submitted: 2026-08-05

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 34× in this paper · explained in 1 episode
• Transformer — 27× in this paper · explained in 2 episodes
• Robustness — 8× in this paper · explained in 5 episodes
• Distillation — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 2× in this paper
• Trace — 2× in this paper · explained in 2 episodes
• Activation Function — 1× in this paper · explained in 1 episode
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Inverse Problem — 1× in this paper · explained in 2 episodes
• Model Merging — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Self-Supervised Learning — 1× in this paper · explained in 8 episodes

Sources

• Block-Recurrent Dynamics in Vision Transformers
• Less is More: Recursive Reasoning with Tiny Networks
• Hierarchical Reasoning Model

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