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    <abstract>Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guidance can significantly enhance the robustness of Vision-Language-Action (VLA) models. However, most existing foundation models have not systematically incorporated goal image conditioning due to the high computational training cost. To this end, we propose Goal-Injected Fine-Tuning (GIFT), a lightweight and efficient fine-tuning framework that seamlessly integrates generated goal images into multiple representative pretrained VLA models. Our approach introduces goal image features into observations via a zero-initialized convolution which progressively grows parameters from zero and prevents harmful noise from disrupting the pretrained policy during fine-tuning. As training proceeds, goal information is gradually incorporated, enabling efficient goal understanding without disrupting model stability. We further introduce a refined image editing method to generate semantically and visually consistent goal images from initial observations and task instructions. Experiments show that goal-aware VLA models achieve substantial performance gains across tasks: with only a single epoch of fine-tuning, GIFT outperforms the base model by 6.0% and 13.4% on two SIMPLER settings, and by 4.7% on LIBERO, demonstrating both efficiency and effectiveness.</abstract>
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%0 Conference Proceedings
%T GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation
%A Fang, Xiaoyuan
%A Feng, Shuo
%A Wang, Yuxuan
%A Cheng, Enhua
%A Zhou, Peng
%A Li, Piji
%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 fang-etal-2026-gift
%X Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guidance can significantly enhance the robustness of Vision-Language-Action (VLA) models. However, most existing foundation models have not systematically incorporated goal image conditioning due to the high computational training cost. To this end, we propose Goal-Injected Fine-Tuning (GIFT), a lightweight and efficient fine-tuning framework that seamlessly integrates generated goal images into multiple representative pretrained VLA models. Our approach introduces goal image features into observations via a zero-initialized convolution which progressively grows parameters from zero and prevents harmful noise from disrupting the pretrained policy during fine-tuning. As training proceeds, goal information is gradually incorporated, enabling efficient goal understanding without disrupting model stability. We further introduce a refined image editing method to generate semantically and visually consistent goal images from initial observations and task instructions. Experiments show that goal-aware VLA models achieve substantial performance gains across tasks: with only a single epoch of fine-tuning, GIFT outperforms the base model by 6.0% and 13.4% on two SIMPLER settings, and by 4.7% on LIBERO, demonstrating both efficiency and effectiveness.
%U https://aclanthology.org/2026.emnlp-main.860/
%P 19903-19922

Markdown (Informal)

[GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation](https://aclanthology.org/2026.emnlp-main.860/) (Fang et al., EMNLP 2026)

• GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation (Fang et al., EMNLP 2026)

ACL

• Xiaoyuan Fang, Shuo Feng, Yuxuan Wang, Enhua Cheng, Peng Zhou, and Piji Li. 2026. GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 19903–19922, Budapest, Hungary. Association for Computational Linguistics.