arXiv:2605.29495v1 Announce Type: new Abstract: Continual supervised fine-tuning (SFT) is the de facto recipe for adapting large language models (LLMs) to a stream of downstream tasks, but it suffers from catastrophic forgetting of earlier capabilities. Recent work shows that on-policy signals -- training on the model's own outputs -- reduce forgetting more reliably than off-policy supervision. Existing on-policy methods route this signal through a new training objective (e.g., self-distillation losses with a teacher copy), inheriting an extra forward pass, schedule sensitivity, and stylistic
The continuous fine-tuning of large language models is a critical bottleneck, as current methods suffer from catastrophic forgetting and are computationally expensive, driving the need for more efficient and robust techniques.
Improving the efficiency and effectiveness of continual supervised fine-tuning directly impacts the adaptability and performance of LLMs, accelerating their deployment across diverse applications and potentially reducing development costs.
This research proposes a new on-policy replay method for LLMs, promising more reliable forgetting reduction and improved stability during continuous fine-tuning compared to existing methods.
- · AI developers
- · Large language model providers
- · Businesses adopting AI agents
- · AI research institutions
- · Companies reliant on static, non-adaptive AI models
More robust and adaptable LLMs can be deployed in a wider range of real-world scenarios without constant retraining.
The cost and complexity of maintaining state-of-the-art LLMs could decrease, democratizing access to advanced AI capabilities.
Accelerated development of AI agents capable of continuous learning and adaptation, impacting various industries and workflows.
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Read at arXiv cs.LG