arXiv:2605.20865v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) plays a pivotal role in improving the reasoning ability of large language models. However, widely used PPO surrogate objectives are fundamentally local, as they rely on a local approximation of the exact policy gradient objective. While this approximation improves stability by reducing the variance induced by importance sampling, it also introduces structural bias into the surrogate objective, which must be controlled through trust region mechanisms. In this work, we introduce the $N$-step for
This research addresses fundamental limitations in current reinforcement learning methods, specifically PPO, which are widely used for training large language models.
Improved reinforcement learning techniques could significantly enhance the reasoning capabilities and performance of large language models, impacting diverse AI applications.
The introduction of multi-step likelihood-ratio correction offers a potential pathway to overcome the structural bias inherent in current policy gradient objectives, leading to more robust and efficient AI training.
- · AI researchers
- · Large language model developers
- · AI-driven product companies
- · Developers reliant on current PPO limitations
- · Companies with less sophisticated AI R&D
More advanced and reliable AI models will become accessible, accelerating AI development.
This could lead to a proliferation of more capable AI agents and automated systems.
The enhanced reasoning capabilities might open new frontiers in scientific discovery and complex problem-solving currently beyond AI's reach.
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Read at arXiv cs.LG