Lang2Act Boosts VLM Visual Reasoning with Emergent Tools
π‘4%+ VLM visual gains via RL-emergent toolsβno rigid engines needed; code live! (62 chars)
β‘ 30-Second TL;DR
What Changed
Introduces self-emergent linguistic toolchains replacing fixed external engines
Why It Matters
Lang2Act bridges perception and reasoning in VLMs, enabling more dynamic tool use and reducing errors in visual tasks. It could inspire adaptive toolchains in multimodal AI, improving real-world VRAG applications.
What To Do Next
Clone https://github.com/NEUIR/Lang2Act and fine-tune on your VLM for emergent visual tools.
Key Points
- β’Introduces self-emergent linguistic toolchains replacing fixed external engines
- β’Two-stage RL training: explore high-quality actions, then exploit for reasoning
- β’Eliminates visual info loss from decoupled perception-reasoning designs
- β’Demonstrates >4% VLM visual perception improvements on benchmarks
- β’Open-source code and data at https://github.com/NEUIR/Lang2Act
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Original source: ArXiv AI β
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