Arxiv paper - InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models

In this episode, we discuss InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models by The authors of the paper "InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models" are as follows: 1. **Jinguo Zhu** 2. **Weiyun Wang** 3. **Zhe Chen** 4. ... InternVL3 advances the InternVL series by jointly training on multimodal and text data in a unified pre-training stage, avoiding the complexities of adapting text-only models to handle visual inputs. It incorporates features like variable visual position encoding and advanced fine-tuning techniques, achieving state-of-the-art performance on benchmarks such as MMMU and competing with leading proprietary models. Committed to open science, the authors plan to publicly release both the training data and model weights to support further research in multimodal large language models.