arXiv:2605.28109v1 Announce Type: new Abstract: Recent advances in online reinforcement learning (RL) for large language models (LLMs) have demonstrated promising performance in complex reasoning tasks. However, they often exhibit an imbalanced exploration-exploitation trade-off, resulting in unstable optimization and sub-optimal performance. We introduce IB-Score, a novel metric grounded in Information Bottleneck theory that evaluates policy's exploration-exploitation balance by quantifying the trade-off between step-level reasoning diversity and mutual information shared with the correct ans
The rapid development and deployment of LLMs in online reinforcement learning necessitate better mechanisms to manage their inherent exploration-exploitation trade-offs.
Improved optimization techniques for LLMs in complex reasoning tasks directly impact the performance and reliability of advanced AI systems, influencing their commercial viability and applications.
The introduction of IB-Score provides a new, theoretically grounded metric to evaluate and potentially stabilize LLM training, leading to more robust and higher-performing AI agents.
- · AI developers
- · Companies using LLMs for complex tasks
- · Reinforcement learning researchers
- · Inefficient LLM training methodologies
- · Organizations relying on sub-optimal LLM implementations
More stable and efficient training of large language models for online reinforcement learning.
Accelerated development of AI agents capable of more sophisticated and reliable reasoning in dynamic environments.
Enhanced automation of complex cognitive tasks, potentially broadening the applicability of AI across numerous white-collar sectors.
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Read at arXiv cs.LG