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Comparing Transformers and Hybrid Models at the Token Level

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arXiv:2606. 20936v1 Announce Type: new Abstract: Hybrid language models that mix attention and recurrent layers have shown promise: theoretically, recurrent layers ameliorate the limitations of pure transformers on state tracking, and empirically, hybrids can outperform pure transformers in loss and downstream evaluations \citep{waleffe2024empirical,merrill2026olmohybrid}.

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Authors: Yanhong Li, William Merrill
Released: 2026-06-23 (16d ago)
Topic: Related to current project
Relation to my work: Read next because Comparing Transformers and Hybrid Models at the Token Level overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: code, word, class, under, eval, prefix, token, rate. Source: arxiv cs.CL (NLP).
arXiv: 2606.20936

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