{"slug": "towards-scalable-rlvr-multimodal-instruction-following-data-synthesis-and", "title": "Towards Scalable RLVR: Multimodal Instruction Following Data Synthesis and Distillation", "summary": "Researchers introduced MIFS (Multimodal Instruction Following Synthesis), a pipeline that generates RL-ready multimodal data for training generalist agents, producing a dataset of 90k samples across 8 constraint categories and 14 task domains. MIFS-trained multimodal large language models (MLLMs) achieved an average improvement of 8.13% on four multimodal instruction following (MMIF) benchmarks and 3x faster training convergence versus using raw data, according to the arXiv:2609.16059v1 paper. The approach combines a generative constraint protocol, a learnability-aware distillation mechanism that filters data based on RL training dynamics, and a code-based verifier for reward signals, mitigating the generalization trade-offs typical of Supervised Fine-Tuning (SFT).", "body_md": "arXiv:2609.16059v1 Announce Type: new \nAbstract: 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 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$\\times$ 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.", "url": "https://wpnews.pro/news/towards-scalable-rlvr-multimodal-instruction-following-data-synthesis-and", "canonical_source": "https://arxiv.org/abs/2609.16059", "published_at": "2026-09-16 04:00:00+00:00", "updated_at": "2026-09-16 04:06:14.535684+00:00", "lang": "en", "topics": ["artificial-intelligence", "machine-learning", "large-language-models", "ai-research", "ai-agents"], "entities": ["MIFS", "Multimodal Instruction Following Synthesis", "arXiv:2609.16059v1", "Reinforcement Learning with Verifiable Rewards", "RLVR", "Supervised Fine-Tuning", "MMIF", "MLLMs"], "alternates": {"html": "https://wpnews.pro/news/towards-scalable-rlvr-multimodal-instruction-following-data-synthesis-and", "markdown": "https://wpnews.pro/news/towards-scalable-rlvr-multimodal-instruction-following-data-synthesis-and.md", "text": "https://wpnews.pro/news/towards-scalable-rlvr-multimodal-instruction-following-data-synthesis-and.txt", "jsonld": "https://wpnews.pro/news/towards-scalable-rlvr-multimodal-instruction-following-data-synthesis-and.jsonld"}}