specific method · filed under post-training
SFT bootstrapping with synthetic data
An initial supervised fine-tuning stage that bootstraps capabilities using synthetic data.
- sources
- 2
- models
- 2
- labs adopt it
- 2
- strongest
- used
How sources treat it
One count per evidence span, weakest treatment to strongest.
used 2
Documented in
Evidence
2 spans quoted from the sources, strongest treatment first.
To bootstrap post-training, we ran an initial SFT on synthetic data generated by open-weights models including Kimi K2.5.
usedpost trainingin InklingThinking Machines Lab
an SFT stage that bootstraps capabilities partly with synthetic data
usedpost trainingin Laguna S 2.1Poolside
Filed alongside
Other methods under post-training :: supervised fine-tuning.
Supervised fine-tuningFine-tuningLight SFT on teacher-distribution dataLoRA fine-tuningRejection samplingSelf-correction cold startAdversarial 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