Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration

Understand

Reinforcement Learning (RL) has become a key approach for enhancing the reasoning capabilities of large language models.

• However, prevalent RL approaches like proximal policy optimization and group relative policy optimization suffer from sparse, outcome-based rewards and weak exploration incentives, limiting their effectiveness.
• Specifically, sparse rewards offer limited feedback, especially on difficult problems, and introduce biases favoring familiar trajectories over novel reasoning paths.
• These issues critically undermine performance on complex tasks that inherently require iterative reasoning.