@inproceedings{hu-etal-2026-safer-steer,
title = "{S}a{F}e{R}-Steer: Evolving Multi-Turn {MLLM}s via Synthetic Bootstrapping and Feedback Dynamics",
author = "Hu, Haolong and
Li, Hanyu and
He, Tiancheng and
Yi, Huahui and
Zhang, An and
Li, Qiankun and
Wang, Kun and
Liu, Yang and
Zeng, Zhigang",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1295/",
pages = "29473--29493",
ISBN = "979-8-89176-459-0",
abstract = "MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploit long-context safety decay. Yet safety alignment is still dominated by single-turn data and fixed-template dialogues, leaving a mismatch between training and deployment.\par To bridge this gap, we propose SaFeR-Steer, a progressive multi-turn alignment framework that combines staged synthetic bootstrapping with tutor-in-the-loop GRPO to train a single student under adaptive, on-policy attacks. We also introduce Trajectory-Consistent Summative Reward (TCSR), which aggregates the historical minimum and average of turn rewards so that any low-quality turn affects the trajectory-level return.\par I. Dataset. We release STEER, a multi-turn multimodal safety dataset with STEER-SFT (12,934), STEER-RL (2,000), and STEER-Bench (3,227) dialogues spanning 1{--}10 turns. II. Experiment. Starting from Qwen2.5-VL-3B/7B, SaFeR-Steer substantially improves Safety/Helpfulness on both single-turn (48.30/45.86 {\textrightarrow} 81.84/70.77 for 3B; 56.21/60.32 {\textrightarrow} 87.89/77.40 for 7B) and multi-turn benchmarks (12.55/27.13 {\textrightarrow} 55.58/70.27 for 3B; 24.66/46.48 {\textrightarrow} 64.89/72.35 for 7B), shifting failures to later turns and yielding robustness beyond scaling alone.\par Code is available at https://github.com/Ed-Bg/SaFeR-Steer-full"
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<abstract>MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploit long-context safety decay. Yet safety alignment is still dominated by single-turn data and fixed-template dialogues, leaving a mismatch between training and deployment.\par To bridge this gap, we propose SaFeR-Steer, a progressive multi-turn alignment framework that combines staged synthetic bootstrapping with tutor-in-the-loop GRPO to train a single student under adaptive, on-policy attacks. We also introduce Trajectory-Consistent Summative Reward (TCSR), which aggregates the historical minimum and average of turn rewards so that any low-quality turn affects the trajectory-level return.\par I. Dataset. We release STEER, a multi-turn multimodal safety dataset with STEER-SFT (12,934), STEER-RL (2,000), and STEER-Bench (3,227) dialogues spanning 1–10 turns. II. Experiment. Starting from Qwen2.5-VL-3B/7B, SaFeR-Steer substantially improves Safety/Helpfulness on both single-turn (48.30/45.86 → 81.84/70.77 for 3B; 56.21/60.32 → 87.89/77.40 for 7B) and multi-turn benchmarks (12.55/27.13 → 55.58/70.27 for 3B; 24.66/46.48 → 64.89/72.35 for 7B), shifting failures to later turns and yielding robustness beyond scaling alone.\par Code is available at https://github.com/Ed-Bg/SaFeR-Steer-full</abstract>
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%0 Conference Proceedings
%T SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics
%A Hu, Haolong
%A Li, Hanyu
%A He, Tiancheng
%A Yi, Huahui
%A Zhang, An
%A Li, Qiankun
%A Wang, Kun
%A Liu, Yang
%A Zeng, Zhigang
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F hu-etal-2026-safer-steer
%X MLLMs are increasingly deployed in multi-turn settings, where attackers can escalate unsafe intent through the evolving visual-text history and exploit long-context safety decay. Yet safety alignment is still dominated by single-turn data and fixed-template dialogues, leaving a mismatch between training and deployment.\par To bridge this gap, we propose SaFeR-Steer, a progressive multi-turn alignment framework that combines staged synthetic bootstrapping with tutor-in-the-loop GRPO to train a single student under adaptive, on-policy attacks. We also introduce Trajectory-Consistent Summative Reward (TCSR), which aggregates the historical minimum and average of turn rewards so that any low-quality turn affects the trajectory-level return.\par I. Dataset. We release STEER, a multi-turn multimodal safety dataset with STEER-SFT (12,934), STEER-RL (2,000), and STEER-Bench (3,227) dialogues spanning 1–10 turns. II. Experiment. Starting from Qwen2.5-VL-3B/7B, SaFeR-Steer substantially improves Safety/Helpfulness on both single-turn (48.30/45.86 → 81.84/70.77 for 3B; 56.21/60.32 → 87.89/77.40 for 7B) and multi-turn benchmarks (12.55/27.13 → 55.58/70.27 for 3B; 24.66/46.48 → 64.89/72.35 for 7B), shifting failures to later turns and yielding robustness beyond scaling alone.\par Code is available at https://github.com/Ed-Bg/SaFeR-Steer-full
%U https://aclanthology.org/2026.emnlp-main.1295/
%P 29473-29493
Markdown (Informal)
[SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics](https://aclanthology.org/2026.emnlp-main.1295/) (Hu et al., EMNLP 2026)
ACL
- Haolong Hu, Hanyu Li, Tiancheng He, Huahui Yi, An Zhang, Qiankun Li, Kun Wang, Yang Liu, and Zhigang Zeng. 2026. SaFeR-Steer: Evolving Multi-Turn MLLMs via Synthetic Bootstrapping and Feedback Dynamics. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 29473–29493, Budapest, Hungary. Association for Computational Linguistics.