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AutoTool: Automatic Scaling of Tool-Use Capabilities in RL via Decoupled Entropy Constraints
arXiv – CS AI|Yirong Zeng, Xiao Ding, Yufei Liu, Yuxian Wang, Qunyao Du, Yutai Hou, Wu Ning, Haonan Song, Duyu Tang, Dandan Tu, Bing Qin, Ting Liu|
🤖AI Summary
Researchers introduce AutoTool, a new reinforcement learning approach that enables AI agents to automatically scale their reasoning capabilities for tool use. The method uses entropy-based optimization and supervised fine-tuning to help models efficiently determine appropriate thinking lengths for simple versus complex problems, achieving 9.8% accuracy improvements while reducing computational overhead by 81%.
Key Takeaways
- →AutoTool addresses key challenges in current RL-based scaling approaches for AI tool use by preventing overthinking on simple problems.
- →The method combines warm-up supervised fine-tuning with entropy-based RL to help models distinguish between simple and complex problems.
- →Entropy-based optimization maintains model diversity while successfully unlocking automatic scaling capabilities.
- →Testing on three benchmarks showed 9.8% accuracy improvements with approximately 81% reduction in computational overhead.
- →The approach enables AI agents to automatically determine appropriate reasoning trajectory lengths for efficient tool use.
#ai-agents#reinforcement-learning#tool-use#autotool#entropy-optimization#computational-efficiency#reasoning#scaling#machine-learning#artificial-intelligence
Read Original →via arXiv – CS AI
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