arXiv:2601.21476v2 Announce Type: replace Abstract: On-policy reinforcement learning (RL) for language model post-training suffers from a fundamental tension: as training progresses, policy entropy collapses and sampling diversity diminishes, causing the model to ``forget'' its own earlier exploratory capacity. While off-policy data can restore diversity, existing methods mix entire trajectories at the sequence level, introducing severe policy mismatch and training instability. We argue that the core question is not \emph{whether} to use off-policy data, but \emph{where} in the sequence it sho
The research addresses a fundamental limitation in reinforcement learning for large language models (LLMs) that becomes increasingly critical as LLM capabilities expand and applications demand more robust and adaptable agents.
Improving the training stability and diversity of LLMs through better off-policy data integration could unlock more sophisticated and reliable AI agents capable of complex, multi-step reasoning and interaction.
Current methods for fine-tuning LLMs often lead to 'forgetting' valuable exploratory behaviors; this research proposes a way to retain diversity and stability by judiciously using historical data, leading to more robust and capable AI agents.
- · AI developers
- · Companies deploying LLM-powered applications
- · Researchers in reinforcement learning
- · Less efficient RL techniques
- · Developers struggling with LLM unreliability
More stable and capable LLMs will accelerate the development of advanced AI agents.
Enhanced AI agents could automate more nuanced tasks, impacting white-collar work and various SaaS layers.
The increased sophistication of autonomous AI systems may raise new ethical and governance questions about their decision-making processes.
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Read at arXiv cs.CL