arXiv Machine Learning
Jun 30
A Mechanistic Study of Transformers Training Dynamics
arXiv:2410. 24050v3 Announce Type: replace Abstract: Large-scale pretraining of transformers has been central to the success of foundation models.
Emergent Capabilities Arise Randomly from Learning Sparse Attention Patterns
arXiv:2606. 25010v1 Announce Type: new Abstract: Neural scaling laws for transformer language models predict smooth improvements in pretraining loss with increasing parameters, but downstream capabilities such as in-context learning are known to emerge abruptly past a certain model scale.
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arXiv Machine Learning
Jul 2
Ghost in the Kernel: In-Context Learning with Efficient Transformers via Domain Generalization
arXiv:2607. 00479v1 Announce Type: new Abstract: Transformer-based large models have demonstrated remarkable generalization abilities across different tasks by leveraging a context-aware attention module for in-context learning.
arXiv Machine Learning
Jul 7
Incremental Learning of Sparse Attention Patterns in Transformers
arXiv:2602. 19143v2 Announce Type: replace Abstract: This paper studies simple transformers trained on a high-order Markov chain, where the model must incorporate information from multiple past positions, each with different statistical importance.
arXiv Machine Learning
Jul 30
Dense Local Dependencies Induce Attention-Logit Explosion and Training Instability During Long-Sequence Transformer Training
arXiv:2505. 15548v2 Announce Type: replace Abstract: Autoregressive transformer language models frequently exhibit training instability when trained on long sequences, particularly under low-precision arithmetic.
arXiv Machine Learning
Jul 7
Sequential Correlations Change In-Context Learning: Effective Context Length and Architectural Mismatch
arXiv:2607. 03660v1 Announce Type: cross Abstract: Modern sequence models have a striking capacity for in-context learning (ICL); they can perform new tasks based only on examples given in the prompt.
arXiv Machine Learning
Jun 29
Prism Transformer: Progressive Head Schedules for Hierarchical Attention Processing
arXiv:2606. 27449v1 Announce Type: new Abstract: Multi-head attention conventionally partitions the hidden dimension equally across all heads at every layer, enforcing an identical representational subspace dimension (dh = dmodel/h) throughout the models depth.
arXiv Machine Learning
Jun 30
Generalization Analysis of Transformers in Distribution Regression
arXiv:2606. 29256v1 Announce Type: cross Abstract: In recent years, models based on the Transformer architecture have seen widespread applications and have become one of the core tools in the field of deep learning.
arXiv Machine Learning
Jul 16
Self-Improving is Often Sudden: Enlightenment-style Finetuning for Large-Scale Models
arXiv:2607. 13395v1 Announce Type: new Abstract: The pursuit of autonomously self-improving models has attracted growing interest in the era of large-scale foundation models.
arXiv Machine Learning
Jun 11
Phase Transitions in Attention: A Bayesian Theory of Copy Head Emergence
arXiv:2606. 12058v1 Announce Type: cross Abstract: Attention is the key mechanism underlying in-context learning in transformers, and attention patterns have been observed empirically to emerge abruptly during training.
arXiv AI
Jun 29
Flexformer: Flexible Linear Transformer with Learnable Attention Kernel
arXiv:2606. 27748v1 Announce Type: cross Abstract: Transformer models rely on attention mechanism to capture long-range dependencies but suffer from quadratic complexity, limiting their scalability to long sequences.
arXiv Machine Learning
Jun 17
Olmo Hybrid: From Theory to Practice and Back
arXiv:2604. 03444v4 Announce Type: replace Abstract: Recent work has demonstrated the potential of non-transformer language models, especially linear recurrent neural networks (RNNs) and hybrid models that mix recurrence and attention.
arXiv Machine Learning
Jun 25
Lifelong In-Context Learning with Transformers Requires Parametric Forms of Attention
arXiv:2606. 25342v1 Announce Type: new Abstract: Lifelong continual learning remains an obstacle on the path to human-like intelligence.