Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning
Sanchit Sinha, Guangzhi Xiong, Bohan Liu, Zhenghao He, Aidong Zhang
Abstract
The effectiveness of Chain-of-Thought (CoT) prompting in Multimodal Large Language Models (MLLMs) remains uncertain: across several visual reasoning benchmarks, CoT prompting often degrades performance compared to direct prompting. In this paper, we provide a systematic analysis of CoT behavior in three modern MLLM families across model scales on datasets requiring step-wise visual evidence. Our analysis identifies two recurring failure modes: premature answer commitment and limited direct visual-token access during rationale generation. We further find that standard CoT-style Supervised Fine-Tuning (CoT-SFT) can mitigate these issues only partially, while often increasing reliance on textual priors and reducing counterfactual visual dependence. Motivated by these findings, we propose Attentive-CoT (Att-CoT), an attention-guided fine-tuning objective that encourages CoT trajectories to delay answer commitment while maintaining sustained visual-token access. Att-CoT can be plugged into any CoT-SFT training run without architectural changes. Experiments on three visual reasoning benchmarks across six MLLMs show that Att-CoT enhances CoT performance over standard fine-tuning.
What this paper contributes
Fine-tuning only a few attention maps, not weights, of multimodal LLMs improves chain-of-thought reasoning. Attention-guided updates steer where the model looks while keeping its knowledge intact, lifting ChartQA and CoT benchmarks at a fraction of full fine-tuning cost.
BibTeX
@misc{sinha2026attention,
title = {Attention-guided Fine-tuning of Multimodal Large Language Models Improves Chain-of-Thought Reasoning},
author = {Sanchit Sinha and Guangzhi Xiong and Bohan Liu and Zhenghao He and Aidong Zhang},
year = {2026},
eprint = {arXiv:2606.01558},
howpublished = {arXiv:2606.01558}
}