Three Tests to Run Before You Switch from LoRA to FullFT

The blog post explores the decision-making process between using LoRA (Low-Rank Adaptation) and Full Parameter Fine-Tuning (FullFT) for training machine learning models, specifically focusing on experiments conducted with the Qwen3.5-9B model across three synthetic tasks: placement, register allocation, and Nexa VM execution. It emphasizes that while FullFT updates every model weight, LoRA only modifies a small adapter, making it faster and more cost-effective for certain tasks. The experiments reveal that LoRA can closely match FullFT performance through adjustments in data coverage, learning rate, and adapter rank, but FullFT retains an edge when task exposure is limited or tasks are more complex. The text advises considering data coverage, recipe optimization, and adapter capacity before switching from LoRA to FullFT, noting that LoRA is often more efficient at higher learning rates and suggesting that FullFT should be used when its quality advantage justifies the increased cost. It also highlights the role of Fireworks Serverless Training and Inference in conducting these experiments, suggesting that LoRA is a practical starting point with the flexibility to expand to FullFT as needed.

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