RAFT: Data Refinement and Adaptive Distillation for Domain Fine-Tuning with Alleviated Forgetting
arXiv:2606.00147v1 Announce Type: new Abstract: Domain-specific supervised fine-tuning (SFT) often improves in-domain performance at the cost of degrading a model's general capabilities. We view this degradation through two practical gaps in domain SFT: a supervision-compatibility gap, where domain targets differ in style and reasoning format from the original model's natural responses, and a trajectory-preservation gap, where teacher-forced SFT optimizes fixed target tokens without constraining the model's behavior on its own generated prefixes. This process fails to preserve the model's orig
The paper addresses a critical challenge in AI development where fine-tuning models for specific tasks often degrades their general capabilities, necessitating solutions for more robust and efficient domain adaptation.
This research provides a method to improve the practical application of large language models by enabling specialized performance without sacrificing foundational general intelligence, crucial for enterprise and strategic AI deployments.
The proposed RAFT method offers a more effective way to fine-tune AI models, potentially leading to more versatile and deployable domain-specific AI systems with reduced risk of 'catastrophic forgetting.'
- · AI developers
- · Enterprises deploying domain-specific AI
- · AI researchers focused on model adaptability
- · Organizations relying on brute-force retraining
- · Developers whose models suffer from severe forgetting
More efficient and effective domain-specific fine-tuning of large language models.
Accelerated adoption of AI in specialized fields due to improved model reliability and performance.
Reduced computational costs and resource demands for deploying tailored AI solutions across various industries.
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