@inproceedings{chen-etal-2026-token,
title = "Token-level Inference-Time Alignment for Vision-Language Models",
author = "Chen, Kejia and
Zheng, Junjun and
Zhang, Jiawen and
Lin, Manxi and
Pan, Xiao and
Hu, Jiacong and
Lou, Jian and
Feng, Zunlei and
Song, Mingli",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1253/",
pages = "25012--25029",
ISBN = "979-8-89176-395-1",
abstract = "Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity, leading to hallucinations where generated text contradicts the image. Countering this bias typically requires resource-heavy fine-tuning or high-latency verification methods that provide feedback only after the full response is generated. To overcome these limitations, we present a framework for Token-level Inference-Time Alignment (TITA) that steers the decoding process without updating the base model parameters. By training a lightweight reward model to capture visual preferences, TITA extracts implicit guidance through log-probability ratios. This approach functions as an inference-time adaptation of Direct Preference Optimization (DPO), injecting dense feedback to correct the output distribution at every generation step. Across diverse architectures including LLaVA-1.5, Qwen3-VL, and InternVL3.5, TITA consistently improves performance on 13 benchmarks. For example, TITA boosts LLaVA-1.5-7B by 8.6{\%} on MMVet and achieves a 74.0 MMStar score with Qwen3-VL-8B. Specifically, these gains incur negligible overhead ({\textasciitilde}0.2s per query), offering a superior trade-off between alignment effectiveness and efficiency. Our code is available at: https://github.com/Thecommonirin/TITA."
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<abstract>Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity, leading to hallucinations where generated text contradicts the image. Countering this bias typically requires resource-heavy fine-tuning or high-latency verification methods that provide feedback only after the full response is generated. To overcome these limitations, we present a framework for Token-level Inference-Time Alignment (TITA) that steers the decoding process without updating the base model parameters. By training a lightweight reward model to capture visual preferences, TITA extracts implicit guidance through log-probability ratios. This approach functions as an inference-time adaptation of Direct Preference Optimization (DPO), injecting dense feedback to correct the output distribution at every generation step. Across diverse architectures including LLaVA-1.5, Qwen3-VL, and InternVL3.5, TITA consistently improves performance on 13 benchmarks. For example, TITA boosts LLaVA-1.5-7B by 8.6% on MMVet and achieves a 74.0 MMStar score with Qwen3-VL-8B. Specifically, these gains incur negligible overhead (~0.2s per query), offering a superior trade-off between alignment effectiveness and efficiency. Our code is available at: https://github.com/Thecommonirin/TITA.</abstract>
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%0 Conference Proceedings
%T Token-level Inference-Time Alignment for Vision-Language Models
%A Chen, Kejia
%A Zheng, Junjun
%A Zhang, Jiawen
%A Lin, Manxi
%A Pan, Xiao
%A Hu, Jiacong
%A Lou, Jian
%A Feng, Zunlei
%A Song, Mingli
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-token
%X Vision-Language Models (VLMs) often prioritize linguistic fluency over visual fidelity, leading to hallucinations where generated text contradicts the image. Countering this bias typically requires resource-heavy fine-tuning or high-latency verification methods that provide feedback only after the full response is generated. To overcome these limitations, we present a framework for Token-level Inference-Time Alignment (TITA) that steers the decoding process without updating the base model parameters. By training a lightweight reward model to capture visual preferences, TITA extracts implicit guidance through log-probability ratios. This approach functions as an inference-time adaptation of Direct Preference Optimization (DPO), injecting dense feedback to correct the output distribution at every generation step. Across diverse architectures including LLaVA-1.5, Qwen3-VL, and InternVL3.5, TITA consistently improves performance on 13 benchmarks. For example, TITA boosts LLaVA-1.5-7B by 8.6% on MMVet and achieves a 74.0 MMStar score with Qwen3-VL-8B. Specifically, these gains incur negligible overhead (~0.2s per query), offering a superior trade-off between alignment effectiveness and efficiency. Our code is available at: https://github.com/Thecommonirin/TITA.
%U https://aclanthology.org/2026.findings-acl.1253/
%P 25012-25029
Markdown (Informal)
[Token-level Inference-Time Alignment for Vision-Language Models](https://aclanthology.org/2026.findings-acl.1253/) (Chen et al., Findings 2026)
ACL
- Kejia Chen, Junjun Zheng, Jiawen Zhang, Manxi Lin, Xiao Pan, Jiacong Hu, Jian Lou, Zunlei Feng, and Mingli Song. 2026. Token-level Inference-Time Alignment for Vision-Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 25012–25029, San Diego, California, United States. Association for Computational Linguistics.