general family · filed under post-training
Fine-tuning
Further training that customizes a pretrained model for a specific task or use case; the evidence does not specify a particular fine-tuning mechanism.
Also called fine-tune.
- sources
- 3
- models
- 3
- labs adopt it
- 2
- strongest
- optional
How sources treat it
One count per evidence span, weakest treatment to strongest.
mentioned 1optional 2
Documented in
Evidence
3 spans quoted from the sources, strongest treatment first.
Fine-tunable: Fully customize models to your specific use case through parameter fine-tuning.
optionalunclearin gpt-oss-120bOpenAI
Hy3 provides a complete model finetuning pipeline
optionalpost trainingin Hy3Tencent Hunyuan
you can fine-tune Gemma 4 to achieve state-of-the-art performance on your specific tasks
mentionedpost trainingin Gemma 4Google
Filed alongside
Other methods under post-training :: supervised fine-tuning.
Supervised fine-tuningLight SFT on teacher-distribution dataLoRA fine-tuningRejection samplingSelf-correction cold startSFT bootstrapping with synthetic dataAdversarial fine-tuningAutoregressive fine-tuningCold-started SFT modelEvaluation-based early stoppingFine-tuning with demographically balanced datasetsFrozen-network warmup for new special tokensInstruction hierarchy trainingInstruction-following fine-tuningLightweight speaker fine-tuningPrompt-based cold start for tool useReasoning-data system promptSafety training to an internal specificationSFT checkpoint for RL researchSFT on MiMo-generated task dataSpecialist-model trainingSupervised fine-tuning at 256k sequence lengthToken-budget-based SFT data blending