Think Outside the Policy: In-Context Steered Policy Optimization

arXiv — cs.LG•Friday, October 31, 2025 at 4:00:00 AM

A recent study highlights advancements in Reinforcement Learning from Verifiable Rewards (RLVR), particularly through methods like Group Relative Policy Optimization (GRPO). These innovations are enhancing the reasoning capabilities of Large Reasoning Models (LRMs), which is crucial for developing more effective AI systems. By addressing the limitations of current methods that restrict exploration, this research opens the door to greater trajectory diversity, potentially leading to more robust AI applications. This progress is significant as it could reshape how AI learns and adapts in complex environments.

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