Minifinetuning: Low-Data Generation Domain Adaptation through Corrective Self-Distillation

Peter Belcak, Greg Heinrich, Jan Kautz, Pavlo Molchanov
NVIDIA Research
Paper Lab
Minifinetuning diagram

An illustration of the MFT setup. The student model (bottom) trains to match corrected soft labels (top-right) of its own unfinetuned predictions produced by the teacher (top-left). Observe that only finetuning data is used (meaning that pre-training general domain data is not necessary), and that the teacher's predictions are customized on a per-token basis to by appropriately τ-corrected for the student's learning.

Abstract

Finetuning language models for a new domain inevitably leads to the deterioration of their general performance. This becomes more pronounced the more limited the finetuning data resource.

We introduce minifinetuning (MFT), a method for language model domain adaptation that considerably reduces the effects of overfitting-induced degeneralization in low-data settings and which does so in the absence of any pre-training data for replay. MFT demonstrates 2-10x more favourable specialization-to-degeneralization ratios than standard finetuning across a wide range of models and domains and exhibits an intrinsic robustness to overfitting when data in the new domain is scarce and down to as little as 500 samples.

Employing corrective self-distillation (see \Cref{figure:diagram}) that is individualized on the sample level, MFT outperforms parameter-efficient finetuning methods, demonstrates replay-like degeneralization mitigation properties, and is composable with either for a combined effect.

Uses & Deployment

MFT is seeing deployment experimentation in NVIDIA's enterprise NIMs and NIM microservices. Here are some of the MFT's industrial advantages that make it a compelling choice for enterprise AI applications:

  • Robust Low-Data Domain Adaptation Without Pretraining Replay. MFT offers a powerful solution to a critical challenge in enterprise AI—customizing large language models for niche or proprietary domains where only limited data is available. Unlike traditional finetuning, which often suffers from catastrophic forgetting, MFT leverages corrective self-distillation to individualize training at the token level, preserving general capabilities while still adapting to new data. This makes it ideal for deployment in enterprise microservices where models must remain competent across a broad range of general tasks while being fine-tuned for specialized, potentially proprietary tasks (e.g., customer support chat, domain-specific search, legal document generation).

  • Operational Efficiency and Compatibility with Existing Techniques. A key advantage of MFT is its independence from architectural changes and its compatibility with existing finetuning approaches like PEFT (e.g., LoRA, DoRA, IA3) and replay. This allows MFT to be integrated into NVIDIA's enterprise microservices pipelines with minimal disruption and maximal modular composability. It can be used standalone to reduce degeneralization without needing access to general-domain replay data, or be combined with methods like replay and PEFT for compounding benefits. In practice, this means MFT can be tailored to match individual customer resource constraints—e.g., memory budgets or data privacy restrictions—while maintaining high model fidelity across tasks.

  • Scalability and Control Over Specialization-Degeneralization Trade-offs: MFT introduces a tunable correction parameter (τ) that governs how much weight is given to domain-specific labels versus the original model's predictions. This tunability is crucial in enterprise deployments where different applications may require different balances between domain fidelity and general-purpose language skills. For example, customer-facing applications might favor general robustness, while internal tools for legal or medical document review might demand higher domain alignment.

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