arXiv:2606.09932v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) has become a standard pipeline for Large Language Model (LLM) post-training. SFT is expected to provide a useful behavioral prior for RL to further enhance model capabilities. However, checkpoints with excessive SFT often show limited improvement during RL. We attribute this failure to the loss of model plasticity: the reduced ability of an SFT-initialized policy to be effectively reshaped by subsequent RL. To better understand this phenomenon, we conduct detailed analysis from
This paper addresses a critical, emerging challenge in current LLM development workflows, signaling a significant technical hurdle for scaling AI models effectively. The research provides a timely analysis as the industry pushes towards more sophisticated RL-based post-training methods.
Understanding and addressing the loss of model plasticity is crucial for the efficient and robust development of large language models. This research directly impacts the future performance and training economics of cutting-edge AI, influencing the pace of innovation.
The optimal workflow for LLM post-training may need significant re-evaluation, moving beyond a simple sequential SFT-then-RL paradigm. New techniques will be required to maintain model plasticity during supervised fine-tuning.
- · AI researchers specializing in model plasticity and RL optimization
- · Companies with advanced capabilities in LLM training and fine-tuning
- · Open-source AI community benefiting from improved training techniques
- · LLM development teams reliant on naive SFT-to-RL pipelines
- · Companies that struggle to adapt to new, more complex training methodologies
This research will immediate lead to an increased focus on developing techniques to preserve or restore model plasticity during the SFT phase.
Improved model plasticity could unlock more effective and efficient RL applications, accelerating advancements in agentic AI capabilities.
More robust and plastic LLMs, trainable with RL, could significantly improve the performance of AI agents, hastening their widespread deployment and impact on white-collar workflows.
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Read at arXiv cs.LG