Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems

📰 ArXiv cs.AI

Learn to optimize reinforcement learning policies using Pass@K, improving performance on harder problems

advanced Published 11 Jun 2026

Action Steps

Implement Pass@K policy optimization to prioritize collective utility of sample sets
Modify existing RL algorithms to optimize for pass@k performance instead of pass@1
Evaluate the impact of Pass@K on exploration and improvement in harder examples
Compare the performance of Pass@K with traditional pass@1 optimization methods
Apply Pass@K to real-world RL problems to demonstrate its effectiveness

Who Needs to Know This

Researchers and engineers working on reinforcement learning can benefit from this approach to improve policy optimization and exploration

Key Insight

💡 Pass@K prioritizes collective utility of sample sets over isolated sample strength, leading to improved exploration and performance on harder problems

Key Takeaways

Learn to optimize reinforcement learning policies using Pass@K, improving performance on harder problems

Full Article

Title: Pass@K Policy Optimization: Solving Harder Reinforcement Learning Problems

Abstract:
arXiv:2505.15201v5 Announce Type: replace-cross Abstract: Reinforcement Learning (RL) algorithms sample multiple n>1 solution attempts for each problem and reward them independently. This optimizes for pass@1 performance and prioritizes the strength of isolated samples at the expense of the diversity and collective utility of sets of samples. This under-utilizes the sampling capacity, limiting exploration and eventual improvement on harder examples. As a fix, we propose Pass-at-k Policy Optimiza

Read full paper → ← Back to Reads