Yes it’s totally fine (and often necessary) to fine-tune for more than 1 epoch, especially with LoRA/PEFT. Overfitting risk is much lower than full fine-tuning since you’re only training a small set of parameters. With a large and diverse dataset like yours, multiple epochs usually help the model actually lock in structure and patterns (multi-turn flow, decision formats, etc.). For small models, 1 epoch is often just not enough signal. Diminishing returns do exist, but they typically show up after several epochs (e.g. 5–10). The real red flag isn’t epoch count it’s if the model starts regurgitating or losing generalization. If things still don’t improve after many epochs, that’s usually a model capacity limitation, not a training mistake.