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CoVe: Training Interactive Tool-Use Agents via Constraint-Guided Verification
arXiv – CS AI|Jinpeng Chen, Cheng Gong, Hanbo Li, Ziru Liu, Zichen Tian, Xinyu Fu, Shi Wu, Chenyang Zhang, Wu Zhang, Suiyun Zhang, Dandan Tu, Rui Liu||2 views
🤖AI Summary
Researchers introduce CoVe, a framework for training interactive tool-use AI agents that uses constraint-guided verification to generate high-quality training data. The compact CoVe-4B model achieves competitive performance with models 17 times larger on benchmark tests, with the team open-sourcing code, models, and 12K training trajectories.
Key Takeaways
- →CoVe framework addresses the challenge of training AI agents for complex, multi-turn tool interactions through constraint-guided data synthesis.
- →The CoVe-4B model achieves 43.0% and 59.4% success rates in Airline and Retail domains respectively on the τ²-bench benchmark.
- →The compact model significantly outperforms similar-scale baselines while remaining competitive with models up to 17 times larger.
- →The framework combines supervised fine-tuning and reinforcement learning with deterministic trajectory verification.
- →Researchers are open-sourcing the complete package including code, trained models, and 12K high-quality training trajectories.
#ai-agents#machine-learning#tool-use#reinforcement-learning#open-source#training-data#benchmarks#efficiency
Read Original →via arXiv – CS AI
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