arXiv:2602.06717v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is commonly based on group sampling to estimate advantages and stabilize policy updates. In practice, computational limits often rule out very large groups, so training proceeds with finite rollout sets that can reinforce only the correct behavior they expose. At practical group sizes, updates can miss rare-correct trajectories while still containing mixed rewards, concentrating probability on more common sampled solutions. We derive the probability of such prompt-local tail-miss events as
This paper addresses a fundamental challenge in Reinforcement Learning (RL) related to sampling efficiency and learning from rare events, which has become more critical with the increasing complexity and scale of AI models and agentic systems.
Improved RL algorithms that can effectively learn from rare but crucial events are vital for creating more robust, efficient, and generalizable AI agents, impacting their reliability and deployment in real-world scenarios.
This research proposes a method (F-GRPO) to mitigate the 'forgetting the rare' problem in RL, potentially leading to more stable and comprehensive policy updates, particularly for complex tasks where critical events are infrequent.
- · AI/ML researchers and developers
- · Developers of AI agents
- · Robotics and autonomous systems companies
- · Industries relying on complex simulations
- · Companies with less sophisticated RL training methods
- · Developers utilizing simpler, less robust RL algorithms
More efficient and reliable training of reinforcement learning models for complex tasks.
Accelerated development of sophisticated AI agents capable of handling a wider range of edge cases and rare scenarios.
Enhanced AI agent autonomy and decision-making in critical applications, potentially reducing human oversight in certain operational contexts.
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Read at arXiv cs.LG