Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization

📰 ArXiv cs.AI

Dual guidance approach for effective experiential learning in reinforcement learning and large language models

advanced Published 26 Mar 2026

Action Steps

Identify external and internal experiences that can guide exploration and gradual improvement in LLMs
Develop a dual guidance framework that combines reinforcement learning from verifiable rewards (RLVR) with internalization techniques
Implement the framework in LLM training to improve reasoning tasks and overall performance
Evaluate the effectiveness of the dual guidance approach through experiments and comparisons with existing methods

Who Needs to Know This

AI engineers and ML researchers can benefit from this approach to improve the capabilities of LLMs, while product managers can apply the insights to develop more effective training methods

Key Insight

💡 Combining external and internal guidance can lead to more effective experiential learning in LLMs

Key Takeaways

Dual guidance approach for effective experiential learning in reinforcement learning and large language models

Full Article

Title: Towards Effective Experiential Learning: Dual Guidance for Utilization and Internalization

Abstract:
arXiv:2603.24093v1 Announce Type: cross Abstract: Recently, reinforcement learning~(RL) has become an important approach for improving the capabilities of large language models~(LLMs). In particular, reinforcement learning from verifiable rewards~(RLVR) has emerged as a promising paradigm for reasoning tasks. However, existing RL-based training still remains only a rough approximation to human learning. Human learners leverage both external and internal experience to guide exploration and gradua

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