The authors designed a post-training recipe for open vision-language models, addressing three bottlenecks: uneven capability coverage, inefficient supervision construction, and cross-domain interference.
Post-training recipe makes compact multimodal reasoners beat larger models
Systematic open-data construction and capacity-aware training lets a 4B model outperform 8B and 9B rivals across 15 benchmarks.
Independent
Juekai Lin · Honglin Lin · Yuqian Yuan · Xiaolong Wu · Jie Cao · Liang Liang · +4 more
Research Digest··2 min read
Lin et al.
Why this paper
Independent
In one line
MMVistaReason demonstrates that systematic open-data construction and capacity-aware post-training provides a practical path to reliable multimodal reasoning.
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- ✓Compute or model size stated (params 4B)
- ·No stated limitations found
- ✓Reports numbers on named benchmarks
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