Projector Is All You Train
The typical training process of a multimodal large language model (MLLM) involves adapting both the language model backbone and the projector between the backbone and a modality-specific encoder. We ask whether fine-tuning the backbone of an MLLM is necessary to adapt it to a new modality. Through experiments on 3D MLLMs, we find that training only the projector is sufficient to achieve strong multimodal performance relative to existing baseline models and our jointly trained MLLMs with the same encoder and backbone. We also show that joint training leads to undesirable drift in existing capabilities of the language model, which projector-only training avoids by definition. Furthermore, p...
| Authors | Nyx Iskandar, Saathvik Selvan, Slater Victoroff |
|---|---|
| Primary category | cs.CL |
| Published | 20 August 2026 |
| Last updated at source | 20 August 2026 |
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