arXiv:2605.12058v2 Announce Type: replace Abstract: Group Relative Policy Optimisation (GRPO) enhances large language models by estimating advantages across a group of sampled trajectories. However, mapping these trajectory-level advantages to policy updates requires aggregating token-level probabilities within each sequence. Relying on a fixed aggregation mechanism for this step fundamentally limits the algorithm's adaptability. Empirically, we observe a critical trade-off: certain fixed aggregations frequently suffer from training collapse, while others fail to yield satisfactory performance
This research is emerging now as the field of large language models rapidly advances, pushing the boundaries of current reinforcement learning optimization techniques.
Improved policy optimization in large language models can lead to more stable and higher-performing AI systems, impacting their general applicability and reliability.
The proposed Holder Policy Optimisation, by addressing the limitations of fixed aggregation mechanisms, offers a more adaptable and potentially robust method for training advanced AI models.
- · AI developers
- · Large Language Model researchers
- · AI-driven product companies
- · Developers relying on less adaptable policy optimization techniques
More stable and performant large language models become available for various applications.
Accelerated development of more complex and reliable AI agents and autonomous systems.
Enhanced AI capabilities could further collapse white-collar workflows, accelerating the adoption of AI agents.
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Read at arXiv cs.LG