Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization
arXiv:2604.11510v2 Announce Type: replace-cross Abstract: To encourage diverse exploration in reinforcement learning (RL) for large language models (LLMs) without compromising accuracy, we propose Policy Split, a novel paradigm that bifurcates the policy into normal and high-entropy modes with a high-entropy prompt. While sharing model parameters, the two modes undergo collaborative dual-mode entropy regularization tailored to distinct objectives. Specifically, the normal mode optimizes for task correctness, while the high-entropy mode incorporates a preference for exploration, and the two mod
The continuous drive for more robust and capable LLMs necessitates novel exploration techniques to overcome limitations in current reinforcement learning approaches.
This development could significantly enhance the efficiency and performance of LLMs in complex tasks by improving their ability to explore diverse solutions without sacrificing accuracy.
The proposed Policy Split paradigm introduces a method for LLMs to balance exploration and exploitation more effectively, potentially leading to more generalized and performant AI agents.
- · AI developers
- · LLM-powered applications
- · AI agents
- · Traditional RL exploration techniques
- · LLMs with limited generalization capabilities
LLMs will become more adept at handling novel situations and complex prompts due to improved exploration.
This could accelerate the deployment of more autonomous and adaptable AI agents across various sectors.
Enhanced LLM capabilities may fuel further innovation in AI research, potentially leading to new breakthroughs in artificial general intelligence.
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Read at arXiv cs.LG