SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice
- URL: http://arxiv.org/abs/2606.04155v1
- Date: Tue, 02 Jun 2026 19:20:54 GMT
- Title: SocialCoach: Personalized Social Skill Learning with RL-based Agentic Tutoring and Practice
- Abstract summary: Social skills such as negotiation and leadership are crucial for personal and professional success in today's interconnected world.<n>SocialCoach is a holistic LLM-powered agentic tutoring system for personalized social skill development at scale.
- Score: 32.96627825091718
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Social skills such as negotiation and leadership are crucial for personal and professional success in today's interconnected world. However, scalable and effective training remains a significant challenge due to the scarcity of expert coaching. In this paper, we introduce SocialCoach, a holistic LLM-powered agentic tutoring system for personalized social skill development at scale. First, SocialCoach automatically constructs a pedagogically-grounded, theory-to-practice knowledge corpus from diverse expert sources, leveraging a multi-agent pipeline. Second, to personalize the learning journey, it employs an adaptive practice scheduling module that follows a prescription-retrieval-adaptation process. To maximize the long-term learning experience while overcoming the cold-start problem, this policy is optimized within a learner simulation environment through reinforcement learning. Finally, SocialCoach integrates immersive, goal-driven practice, causality-driven proficiency assessment and knowledge-grounded, reflective tutoring to help address the knowing-doing gap. We deploy it in our product, EQoach, and conduct extensive experiments. The results show that SocialCoach improves simulated pathway quality and judge-rated tutoring quality over baseline approaches, while early user feedback indicates strong perceived engagement and usefulness. These findings suggest a practical architecture for personalized and gamified pedagogical platforms on soft skill learning.
Related papers
- MARO: Learning Stronger Reasoning from Social Interaction [7.77506109184819]
Multi-Agent Reward Optimization (MARO) is a method that enables large language models to acquire stronger reasoning abilities.<n> Experimental results demonstrate that MARO achieves significant improvements in social reasoning capabilities.
arXiv Detail & Related papers (2026-01-18T09:10:08Z) - LLM-powered Multi-agent Framework for Goal-oriented Learning in Intelligent Tutoring System [54.71619734800526]
GenMentor is a multi-agent framework designed to deliver goal-oriented, personalized learning within ITS.<n>It maps learners' goals to required skills using a fine-tuned LLM trained on a custom goal-to-skill dataset.<n>GenMentor tailors learning content with an exploration-drafting-integration mechanism to align with individual learner needs.
arXiv Detail & Related papers (2025-01-27T03:29:44Z) - Social Skill Training with Large Language Models [65.40795606463101]
People rely on social skills like conflict resolution to communicate effectively and to thrive in both work and personal life.
This perspective paper identifies social skill barriers to enter specialized fields.
We present a solution that leverages large language models for social skill training via a generic framework.
arXiv Detail & Related papers (2024-04-05T16:29:58Z) - SOTOPIA-$π$: Interactive Learning of Socially Intelligent Language Agents [73.35393511272791]
We propose an interactive learning method, SOTOPIA-$pi$, improving the social intelligence of language agents.
This method leverages behavior cloning and self-reinforcement training on filtered social interaction data according to large language model (LLM) ratings.
arXiv Detail & Related papers (2024-03-13T17:17:48Z) - RLIF: Interactive Imitation Learning as Reinforcement Learning [56.997263135104504]
We show how off-policy reinforcement learning can enable improved performance under assumptions that are similar but potentially even more practical than those of interactive imitation learning.
Our proposed method uses reinforcement learning with user intervention signals themselves as rewards.
This relaxes the assumption that intervening experts in interactive imitation learning should be near-optimal and enables the algorithm to learn behaviors that improve over the potential suboptimal human expert.
arXiv Detail & Related papers (2023-11-21T21:05:21Z) - Training Socially Aligned Language Models on Simulated Social
Interactions [99.39979111807388]
Social alignment in AI systems aims to ensure that these models behave according to established societal values.
Current language models (LMs) are trained to rigidly replicate their training corpus in isolation.
This work presents a novel training paradigm that permits LMs to learn from simulated social interactions.
arXiv Detail & Related papers (2023-05-26T14:17:36Z) - Social learning spontaneously emerges by searching optimal heuristics
with deep reinforcement learning [0.0]
We employ a deep reinforcement learning model to optimize the social learning strategies of agents in a cooperative game in a multi-dimensional landscape.
We find that the agent spontaneously learns various concepts of social learning, such as copying, focusing on frequent and well-performing neighbors, self-comparison, and the importance of balancing between individual and social learning.
We demonstrate the superior performance of the reinforcement learning agent in various environments, including temporally changing environments and real social networks.
arXiv Detail & Related papers (2022-04-26T15:10:27Z) - Emergent Social Learning via Multi-agent Reinforcement Learning [91.57176641192771]
Social learning is a key component of human and animal intelligence.
This paper investigates whether independent reinforcement learning agents can learn to use social learning to improve their performance.
arXiv Detail & Related papers (2020-10-01T17:54:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.