arXiv:2605.21125v1 Announce Type: new Abstract: Group Relative Policy Optimization (GRPO), a prominent algorithm within the Reinforcement Learning from Verifiable Rewards (RLVR) framework, has achieved strong results in improving the reasoning capabilities of large language models (LLMs). However, GRPO is prone to advantage collapse, a failure mode where homogeneous rewards within a group (e.g., all correct or all incorrect answers) yield near-zero advantages and vanishing gradients. To address this, we introduce the Advantage Collapse Rate (ACR), the first diagnostic metric quantifying the pr
The rapid development and deployment of large language models are exposing critical failure modes in established reinforcement learning algorithms like GRPO, necessitating immediate diagnostic and mitigation strategies.
Improved reinforcement learning from verifiable rewards is key to enhancing the reasoning capabilities of LLMs, which underpins the development of more robust AI agents and sovereign AI initiatives.
The identification and proposed mitigation of 'advantage collapse' in GRPO promise to make LLM training more efficient and effective, reducing the computational resources and time required to achieve desired model performance.
- · AI developers
- · Large Language Models (LLMs)
- · Cloud providers
- · Companies adopting advanced AI agents
- · Inefficient AI training methods
- · Organizations heavily reliant on unoptimized RLVR
More reliable and less resource-intensive training of advanced AI models will accelerate their deployment.
The improved reasoning capabilities of LLMs will lead to a broader application of AI agents in complex tasks, collapsing more white-collar workflows.
Enhanced AI capabilities could contribute to national efforts in building independent AI infrastructure, reducing reliance on external AI stacks.
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Read at arXiv cs.LG