specific method · filed under post-training
Autoregressive fine-tuning
Fine-tuning a vision encoder connected to a language model using a next-token prediction objective.
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In the autoregressive fine-tuning stage, we connect the vision encoder to a 4B MoE LLM and train on 236B tokens across datasets including image captions, alt text, charts, and OCR, using a next-token prediction objective.
usedunclearin DeepSeek-V4.1-FlashDeepSeek
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Other methods under post-training :: supervised fine-tuning.
Supervised fine-tuningFine-tuningLight SFT on teacher-distribution dataLoRA fine-tuningRejection samplingSelf-correction cold startSFT bootstrapping with synthetic dataAdversarial 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