SOURCE-LINKED INTELLIGENCE

Towards Scalable RLVR: Multimodal Instruction Following Data Synthesis and Distillation

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Multimodal instruction following (MMIF) is crucial for building generalist agents. However, current training paradigms rely heavily on Supervised Fine-Tuning (SFT), which often leads to surface-level pattern matching and degrades general capabilities. While Reinforcement Learning with Verifiable Rewards (RLVR) offers a promising alternative, its scalability in MMIF is severely bottlenecked by the scarcity of high-quality, RL-ready multimodal data. To bridge this gap, we present MIFS (\textbf{M}ultimodal \textbf{I}nstruction \textbf{F}ollowing \textbf{S}ynthesis), a systematic pipeline designed

Read original source ↗ Open in workspace

recordType

paper

region

Global