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New research explores transformer instability in long-context models

By PulseAugur Editorial · [2 sources] ·

Two recent arXiv papers delve into the challenges of training transformers with long contexts. The first paper identifies a fundamental pathology called rank-collapse, where attention scores become uniform as context length increases, and proposes "attention scaling" with a critical factor of \log n to maintain adaptive attention. The second paper attributes training instability in long-sequence transformers, particularly with low-precision arithmetic, to dense local dependencies. This paper suggests that these dependencies lead to an "attention-logit explosion" which can be mitigated by increasing the attention dimension or explicitly modeling local dependencies. AI

IMPACT These papers offer theoretical insights into improving the stability and efficiency of long-context transformer models, potentially leading to more capable LLMs.

RANK_REASON Two academic papers published on arXiv detailing theoretical and empirical findings on transformer architecture limitations.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research explores transformer instability in long-context models

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Two academic papers published on arXiv detailing theoretical and empirical findings on transformer architecture limitations.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Shi Chen, Zhengjiang Lin, Yury Polyanskiy, Philippe Rigollet ·

    Critical attention scaling in long-context transformers

    arXiv:2510.05554v2 Announce Type: replace Abstract: As large language models scale to longer contexts, attention layers suffer from a fundamental pathology: attention scores collapse toward uniformity as context length $n$ increases, causing tokens to cluster excessively, a pheno…

  2. arXiv cs.LG TIER_1 English(EN) · Suvadeep Hajra ·

    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. Although this instability is often accompanied by attention-logit ex…

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