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

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.

A Reproducibility Study of Metacognitive Retrieval-Augmented Generation

Recently, Retrieval Augmented Generation (RAG) has shifted focus to multi-retrieval approaches to tackle complex tasks such as multi-hop question answering. However, these systems struggle to decide when to stop searching once enough information has been gathered. To address this, \citet{zhou2024metacognitive} introduced Metacognitive Retrieval Augmented Generation (MetaRAG), a framework inspired by metacognition that enables Large Language Models to critique and refine their reasoning. In this reproducibility paper, we reproduce MetaRAG following its original experimental setup and extend it in two directions: (i) by evaluating the effect of PointWise and ListWise rerankers, and (ii) by comparing with SIM-RAG, which employs a lightweight critic model to stop retrieval. Our results confirm MetaRAG's relative improvements over standard RAG and reasoning-based baselines, but also reveal lower absolute scores than reported, reflecting challenges with closed-source LLM updates, missing implementation details, and unreleased prompts. We show that MetaRAG is partially reproduced, gains substantially from reranking, and is more robust than SIM-RAG when extended with additional retrieval features.