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 Optimization (PKPO), a transformation on the final rewards which leads to direct optimization of pass@k perf
The continuous push for more robust and capable AI solutions, especially in reinforcement learning, drives innovation that addresses current limitations in diverse problem sets.
This development improves how AI agents learn from multiple solution attempts, potentially leading to more effective and generalizable AI, particularly for complex tasks where exploration and diversity are crucial.
Reinforcement Learning algorithms can now be optimized for the collective utility of samples rather than just individual best attempts, enhancing exploration and the ability to solve harder problems.
- · AI developers
- · Robotics companies
- · Complex problem-solving domains
- · Generative AI models
- · AI systems relying solely on pass@1 optimization
- · Brute-force optimization methods
Improved performance of AI agents in simulated and real-world environments.
Faster development and deployment of autonomous systems capable of handling more varied and difficult tasks.
Enhanced AI capabilities accelerating breakthroughs in scientific discovery and complex engineering challenges.
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Read at arXiv cs.CL