arXiv:2510.18874v3 Announce Type: replace Abstract: Adapting language models (LMs) to new tasks via post-training carries the risk of degrading existing capabilities -- a phenomenon classically known as catastrophic forgetting. In this paper, toward identifying guidelines for mitigating this phenomenon, we systematically compare the forgetting patterns of two widely adopted post-training methods: supervised fine-tuning (SFT) and reinforcement learning (RL). Our experiments reveal a consistent trend across LM families (Llama, Qwen) and tasks (instruction following, general knowledge, and arithm
The rapid advancement and deployment of large language models are highlighting critical scaling and stability challenges, particularly around continuous learning and avoiding 'catastrophic forgetting.'
Mitigating catastrophic forgetting is crucial for developing robust and continuously adaptable AI, directly impacting the economic viability and practical applicability of advanced AI systems in real-world scenarios.
This research provides deeper insight into specific post-training methods (SFT vs. RL) and their impact on model retention, offering actionable guidance for AI developers aiming to build more stable and persistent capabilities.
- · AI model developers
- · Enterprises deploying AI
- · Reinforcement learning researchers
- · AI models prone to forgetting
- · Developers using suboptimal training methods
Improved methods for continuous learning in LLMs will accelerate their integration into dynamic operational environments.
More stable and adaptable AI agents will emerge, capable of retaining knowledge while learning new tasks, enhancing automation capabilities.
The reduced need for periodic retraining or complete model overhauls could lead to significant cost savings and faster iteration cycles for AI deployment.
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Read at arXiv cs.LG