Multimodal instruction following is essential for generalist agents, yet current training relies heavily on Supervised Fine-Tuning which often results in surface-level pattern matching.
To address data scarcity, researchers introduced MIFS, a systematic pipeline that uses a generative constraint protocol to synthesize diverse raw samples. A learnability-aware distillation mechanism subsequently filters this data based on RL training dynamics to ensure stable policy optimization.
Concrete Results
The resulting dataset contains 90k samples spanning 8 constraint categories and 14 task domains. Empirical evaluations show that MIFS-trained Multimodal Large Language Models achieve an average improvement of 8.13% on four MMIF benchmarks.
Training convergence is significantly accelerated, reaching stability three times faster compared to training with raw data. A code-based verifier provides high-precision reward signals necessary for this policy learning process.
Operational Impact
This approach mitigates the generalization trade-offs typical of SFT while preserving core visual capabilities and boosting instruction-following precision. The methodology offers a scalable alternative to traditional supervised fine-tuning for multimodal agents.
Source: https://arxiv.org/abs/2609.16059
