Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training

• URL: http://arxiv.org/abs/2606.11854v1
• Date: Wed, 10 Jun 2026 09:30:37 GMT
• Title: Fine-tuning Multi-modal LLMs with ART: Art-based Reinforcement Training
• Abstract summary: We propose fine-tuning with ART (Art-based Reinforcement Training)<n>The method injects information into a frozen Multimodal Large Language Model (MLLM) by optimizing only its raw visual input.<n>It relies on backpropagation of gradients back into a plain pixel array and thus supports any fine-tuning objective.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are two main Parameter-Efficient Fine-Tuning (PEFT) techniques for Large Language Models (LLMs). While Low-Rank Adaptation (LoRA) introduces additional weights between the LLM layers, Soft Prompting introduces additional fine-tuning-specific raw tokens to an LLM input. However, both require modification to the computational graphs of precompiled, preoptimized LLMs. As a result, neither is fully supported in high-throughput engines like vLLM. We propose fine-tuning with ART (Art-based Reinforcement Training). The method injects information into a frozen Multimodal Large Language Model (MLLM) by optimizing only its raw visual input, thus enabling the soft-token approach on pre-compiled computational graphs. It relies on backpropagation of gradients back into a plain pixel array and thus supports any fine-tuning objective. Moreover, the optimized visual input can be stylized as task-relevant computational artworks. The approach's effectiveness is confirmed for different sizes of a popular open Qwen architecture and for several textual benchmarks. Specifically, ART reaches accuracy competitive with LoRA across mathematics and structured-tool-use benchmarks.

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