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After the Supervised Fine-Tuning (SFT) phase, we observed a notable degradation in the instruction-following capabilities of the LLaVA Multi-Modal Large Language Model (MM-LLM). To address this issue, we introduced a 6K-entry VQA preference dataset and employed Direct Preference Optimization (DPO), alongside testing other algorithms such as Rejection Sampling and SteerLM, to enhance instruction-following proficiency. Our methodology not only fully restored the language following capabilities of LLaVa on the MT-Bench but also outperformed LLaVA-RLHF and Vicuna. Additionally, our approach extended to visual VQA tasks, as demonstrated by significant performance improvements on MM-Vet and LLaVa-Bench. An interesting observation was that, compared to models using distilled SFT, our method showed substantial out-of-distribution improvements. 
After the Supervised Fine-Tuning (SFT) phase, we observed a notable degradation in the instruction-following capabilities of the LLaVA Multi-Modal Large Language Model (MM-LLM). To address this issue, we introduced a 6K-entry VQA preference dataset and employed Direct Preference Optimization (DPO), alongside testing other algorithms such as Rejection Sampling and SteerLM, to enhance instruction-following proficiency. Our methodology not only fully restored the language following capabilities of LLaVa on the MT-Bench but also outperformed LLaVA-RLHF and Vicuna. Additionally, our approach extended to visual VQA tasks, as demonstrated by significant performance improvements on MM-Vet and LLaVa-Bench. An interesting observation was that, compared to models using distilled SFT, our method showed substantial out-of-distribution improvements. 
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https://arxiv.org/abs/2402.10884
https://arxiv.org/abs/2402.10884
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Model available
Model available
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resource
https://huggingface.co/alexshengzhili/llava-v1.5-13b-dpo
model
alexshengzhili/llava-v1.5-13b-dpo
https://huggingface.co/alexshengzhili/llava-v1.5-13b-dpo
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GitHub: 
GitHub: 
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link
https://github.com/findalexli/mllm-dpo/edit/main/README.MD
https://github.com/findalexli/mllm-dpo/edit/main/README.MD

After the Supervised Fine-Tuning (SFT) phase, we observed a notable degradation in the instruction-following capabilities of the LLaVA Multi-Modal Large Language Model (MM-LLM). To address this issue, we introduced a 6K-entry VQA preference dataset and employed Direct Preference Optimization (DPO), alongside testing other algorithms such as Rejection Sampling and SteerLM, to enhance instruction-following proficiency. Our methodology not only fully restored the language following capabilities of LLaVa on the MT-Bench but also outperformed LLaVA-RLHF and Vicuna. Additionally, our approach extended to visual VQA tasks, as demonstrated by significant performance improvements on MM-Vet and LLaVa-Bench. An interesting observation was that, compared to models using distilled SFT, our method showed substantial out-of-distribution improvements. 

https://arxiv.org/abs/2402.10884
Model available
https://huggingface.co/alexshengzhili/llava-v1.5-13b-dpo
GitHub: 
https://github.com/findalexli/mllm-dpo/edit/main/README.MD
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