arXiv:2606.15455v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a key approach for enhancing the reasoning abilities of large language models. However, RLVR often suffers from \emph{diversity collapse}: Pass@$1$ improves while high-$k$ Pass@$k$ degrades, which is viewed as a narrowing of the model's reasoning boundary. We formalize this diversity collapse through the lens of \emph{overtraining}: once a problem's contribution to the reference metric has effectively saturated, further updates no longer expand what the model can solve but still c
The rapid advancement and deployment of large language models (LLMs) and reinforcement learning techniques make understanding their limitations, such as diversity collapse, crucial for continued progress.
This research provides a formal understanding of a critical failure mode in advanced AI training, directly impacting the reliability and generality of LLMs and autonomous AI agents.
The formalization of 'overtraining' as the cause of diversity collapse in RLVR offers a new lens for developing more robust and generalizable AI models, moving beyond simple performance metrics.
- · AI researchers
- · Developers of advanced LLMs
- · AI safety and alignment initiatives
- · Organizations relying solely on Pass@1 metrics
- · Undisciplined AI development favoring speed over robustness
Improved understanding of LLM training failures leads to more effective mitigation strategies for diversity collapse.
Development of new reinforcement learning algorithms and regularization techniques specifically designed to counteract overtraining and promote diversity.
More generalized and less brittle AI models capable of solving a wider range of complex problems without degraded performance on high-k tasks, accelerating the development of highly capable AI agents.
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Read at arXiv cs.AI