arXiv:2602.15620v5 Announce Type: replace Abstract: Reinforcement Learning (RL) has significantly improved large language model reasoning, but existing RL fine-tuning methods rely heavily on heuristic techniques such as entropy regularization and reweighting to maintain stability. In practice, they often suffer from late-stage performance collapse, leading to degraded reasoning quality and unstable training. We identify a key factor behind this instability: a small fraction of tokens, termed spurious tokens (around 0.01%), which contribute little to the reasoning outcome but receive disproport
This paper addresses a critical, current challenge of instability in Reinforcement Learning for Large Language Models (RLHF), which is a key method for improving AI reasoning capabilities.
Improving the stability and reliability of RL for LLMs can significantly accelerate the development of more capable and deployable AI systems, directly impacting the performance ceiling of advanced AI.
This research suggests a more robust method for fine-tuning LLMs, potentially leading to faster training, reduced computational waste, and more consistent performance in AI models.
- · AI model developers
- · Companies deploying LLMs
- · Researchers in reinforcement learning
- · Inefficient RLHF methodologies
- · Users experiencing unstable LLM outputs
More stable and performant large language models become available for various applications.
Accelerated deployment of advanced AI agents and systems due to improved reliability and reasoning.
This could contribute to an overall increase in investment and development within the autonomous AI systems sector.
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Read at arXiv cs.CL