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Adaptive Social Learning via Mode Policy Optimization for Language Agents

arXiv – CS AI|Minzheng Wang, Yongbin Li, Haobo Wang, Xinghua Zhang, Nan Xu, Bingli Wu, Fei Huang, Haiyang Yu, Wenji Mao|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers propose an Adaptive Social Learning (ASL) framework with Adaptive Mode Policy Optimization (AMPO) algorithm to improve language agents' reasoning abilities in social interactions. The system dynamically adjusts reasoning depth based on context, achieving 15.6% higher performance than GPT-4o while using 32.8% shorter reasoning chains.

Key Takeaways

→ASL framework enables language agents to dynamically adjust reasoning depth in social scenarios rather than using uniform approaches.
→AMPO algorithm outperforms existing methods like GRPO by 7.0% while requiring significantly shorter thinking chains.
→The system demonstrates 15.6% higher task performance compared to GPT-4o in social intelligence benchmarks.
→Framework addresses token efficiency issues in current AI reasoning systems through adaptive depth control.
→Research advances multi-granular reasoning mode design and context-aware switching capabilities for AI agents.

#artificial-intelligence #language-models #adaptive-learning #reasoning #social-intelligence #optimization #gpt-4 #token-efficiency #machine-learning #research

Read Original →via arXiv – CS AI

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