Squeezing More from Limited Data with Recursive Transformers

arXiv:2608.26973 · cs.CL, cs.LG · Submitted 2026-08-27 · Read on arXiv

cs.CL, cs.LG

Submitted: 2026-08-27

Updated: 2026-08-27

Code: https://github.com/serdardoesml/recursive-lm

Terminology

• Transformer — 44× in this paper · explained in 2 episodes
• Embeddings — 28× in this paper · explained in 2 episodes
• Benchmark — 8× in this paper · explained in 2 episodes
• Ablation — 4× in this paper · explained in 1 episode
• Generalization — 4× in this paper · explained in 13 episodes
• Adam — 3× in this paper · explained in 2 episodes
• Associative Memory — 2× in this paper · explained in 2 episodes
• Regularization — 2× in this paper · explained in 3 episodes
• Scaling Laws — 2× in this paper · explained in 4 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Clustering — 1× in this paper · explained in 2 episodes
• Empirical Methods in Natural Language Processing — 1× in this paper
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Latent Reasoning — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Sparsity — 1× in this paper · explained in 4 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes

Sources

• AbbIE: Autoregressive Block-Based Iterative Encoder for Efficient Sequence Modeling
• Adaptive Computation Time for Recurrent Neural Networks
• NorMuon: Making Muon more efficient and scalable
• Less is More: Recursive Reasoning with Tiny Networks
• Scaling Laws for Neural Language Models
• Parcae: Scaling Laws For Stable Looped Language Models
• Hierarchical Reasoning Model
• Understanding Transformer from the Perspective of Associative Memory

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