@inproceedings{gautam-etal-2026-consistency,
title = "Consistency Training Along the Transformer Stack",
author = "Gautam, Sukrati and
Shah, Neil and
Dhoot, Arav and
Maruyama, Bryan and
Wei, Caroline and
Kapoor, Rohan and
Sidey, Robert and
Gupta, Prakhar and
Huang, Zi Cheng and
Africa, David Demitri",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.472/",
pages = "9886--9924",
ISBN = "979-8-89176-462-0",
abstract = "Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. We broaden the scope of consistency training in two ways. First, we introduce two new internal consistency targets: MLP Consistency Training (MLPCT), which matches post-activation MLP states, and Attention Consistency Training (AttCT), which matches per-head attention distributions. Second, we apply consistency training to four additional safety threats: persona in-context learning attacks, adversarial frustration, prefill attacks, and conditional misalignment. Across several models and threat settings, we find that consistency training reduces misalignment well beyond the sycophancy and jailbreak settings studied in prior work. The effective consistency target depends on the structure of the threat: representation-level targets suit wrapper-style failures, while output-level BCT is the reliable choice for trajectory-level failures such as prefill, frustration, and conditional misalignment. We also find cases of cross-threat generalization, where training against one failure mode improves robustness to another, and identify a shared residual-stream mechanism underlying ACT, MLPCT, and AttCT, while distinguishing BCT as mechanistically distinct. Our results suggest that consistency training is a flexible and extensible framework for alignment, capable of unifying defenses against a broader class of model pathologies."
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<abstract>Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. We broaden the scope of consistency training in two ways. First, we introduce two new internal consistency targets: MLP Consistency Training (MLPCT), which matches post-activation MLP states, and Attention Consistency Training (AttCT), which matches per-head attention distributions. Second, we apply consistency training to four additional safety threats: persona in-context learning attacks, adversarial frustration, prefill attacks, and conditional misalignment. Across several models and threat settings, we find that consistency training reduces misalignment well beyond the sycophancy and jailbreak settings studied in prior work. The effective consistency target depends on the structure of the threat: representation-level targets suit wrapper-style failures, while output-level BCT is the reliable choice for trajectory-level failures such as prefill, frustration, and conditional misalignment. We also find cases of cross-threat generalization, where training against one failure mode improves robustness to another, and identify a shared residual-stream mechanism underlying ACT, MLPCT, and AttCT, while distinguishing BCT as mechanistically distinct. Our results suggest that consistency training is a flexible and extensible framework for alignment, capable of unifying defenses against a broader class of model pathologies.</abstract>
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%0 Conference Proceedings
%T Consistency Training Along the Transformer Stack
%A Gautam, Sukrati
%A Shah, Neil
%A Dhoot, Arav
%A Maruyama, Bryan
%A Wei, Caroline
%A Kapoor, Rohan
%A Sidey, Robert
%A Gupta, Prakhar
%A Huang, Zi Cheng
%A Africa, David Demitri
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F gautam-etal-2026-consistency
%X Consistency training encourages models to behave similarly across different contexts, and has shown promise for reducing misalignment. We broaden the scope of consistency training in two ways. First, we introduce two new internal consistency targets: MLP Consistency Training (MLPCT), which matches post-activation MLP states, and Attention Consistency Training (AttCT), which matches per-head attention distributions. Second, we apply consistency training to four additional safety threats: persona in-context learning attacks, adversarial frustration, prefill attacks, and conditional misalignment. Across several models and threat settings, we find that consistency training reduces misalignment well beyond the sycophancy and jailbreak settings studied in prior work. The effective consistency target depends on the structure of the threat: representation-level targets suit wrapper-style failures, while output-level BCT is the reliable choice for trajectory-level failures such as prefill, frustration, and conditional misalignment. We also find cases of cross-threat generalization, where training against one failure mode improves robustness to another, and identify a shared residual-stream mechanism underlying ACT, MLPCT, and AttCT, while distinguishing BCT as mechanistically distinct. Our results suggest that consistency training is a flexible and extensible framework for alignment, capable of unifying defenses against a broader class of model pathologies.
%U https://aclanthology.org/2026.findings-emnlp.472/
%P 9886-9924
Markdown (Informal)
[Consistency Training Along the Transformer Stack](https://aclanthology.org/2026.findings-emnlp.472/) (Gautam et al., Findings 2026)
ACL
- Sukrati Gautam, Neil Shah, Arav Dhoot, Bryan Maruyama, Caroline Wei, Rohan Kapoor, Robert Sidey, Prakhar Gupta, Zi Cheng Huang, and David Demitri Africa. 2026. Consistency Training Along the Transformer Stack. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 9886–9924, Budapest, Hungary. Association for Computational Linguistics.