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 (M Multimodal Instruction Following Synthesis), a systematic pipeline designed to generate RL-ready multimodal data.
Specifically, MIFS introduces a generative constraint protocol to synthesize diverse raw samples, followed by a learnability-aware distillation mechanism that filters data based on RL training dynamics to ensure stable policy optimization.
Furthermore, a code-based verifier provides high-precision reward signals for policy learning.
The resulting dataset comprises 90k samples across 8 constraint categories and 14 task domains.
Empirical evaluations demonstrate that MIFS-trained MLLMs achieve an average improvement of 8.13% on four MMIF benchmarks and a 3× faster training convergence compared to using raw data.
Crucially, our approach mitigates the generalization trade-offs typical of SFT, preserving core visual capabilities while significantly boosting instruction-following precision.