SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice

arXiv:2606.04155 · cs.HC, cs.CL, cs.CY · Submitted 2026-08-16 · Read on arXiv

Tianfu Wang, Max Xiong, Jianxun Lian, Hongyuan Zhu, Zhengyu Hu, Yuxuan Lei, Linxiao Gong, Dapeng Hu, Xiaofang Li, Peiting Tsai, Nicholas Jing Yuan, Qi Zhang

cs.HC, cs.CL, cs.CY

Submitted: 2026-08-16

Updated: 2026-08-18

Code: https://github.com/GeminiLight/SocialCoach

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Personalization — 16× in this paper · explained in 2 episodes
• Adaptation — 12× in this paper · explained in 1 episode
• Alignment — 11× in this paper · explained in 1 episode
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Scaffolding — 8× in this paper · explained in 3 episodes
• Consistency — 6× in this paper · explained in 3 episodes
• Core — 6× in this paper · explained in 2 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Actionability — 5× in this paper · explained in 3 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Hallucination — 3× in this paper · explained in 3 episodes
• RAG — 3× in this paper · explained in 3 episodes
• Transferability — 3× in this paper · explained in 4 episodes
• Artificial Intelligence — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• Trace — 2× in this paper · explained in 2 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Actio — 1× in this paper · explained in 1 episode
• Activation Steering — 1× in this paper · explained in 4 episodes
• Actor — 1× in this paper · explained in 3 episodes
• Agent Architectures — 1× in this paper · explained in 1 episode
• Agentic Framework — 1× in this paper · explained in 6 episodes
• Agentic System — 1× in this paper · explained in 2 episodes
• Artificial Intelligence (AI) — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Contrastive Learning — 1× in this paper · explained in 5 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Intelligent Tutoring Systems — 1× in this paper
• LLM-as-a-Judge — 1× in this paper · explained in 3 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Multi-Agent System — 1× in this paper · explained in 5 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Reward Shaping — 1× in this paper · explained in 2 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Steering — 1× in this paper · explained in 2 episodes
• World Model — 1× in this paper · explained in 7 episodes

Sources

• From Persona to Personalization: A Survey on Role-Playing Language Agents
• LLM Agents for Education: Advances and Applications
• From Problem-Solving to Teaching Problem-Solving: Aligning LLMs with Pedagogy using Reinforcement Learning
• Scaling Synthetic Data Creation with 1,000,000,000 Personas
• A Survey on LLM-as-a-Judge
• SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors
• User Modeling and User Profiling: A Comprehensive Survey
• UserRL: Training Interactive User-Centric Agent via Reinforcement Learning
• Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models
• DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
• Reinforcement Learning Optimization for Large-Scale Learning: An Efficient and User-Friendly Scaling Library
• Qwen3 Technical Report
• Social Skill Training with Large Language Models
• Sotopia-RL: Reward Design for Social Intelligence
• SocioVerse: A World Model for Social Simulation Powered by LLM Agents and A Pool of 10 Million Real-World Users
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• Teaching Language Models to Evolve with Users: Dynamic Profile Modeling for Personalized Alignment
• A Comprehensive Survey of Reward Models: Taxonomy, Applications, Challenges, and Future

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