Sherlock: Self-Correcting Reasoning in Vision-Language Models
Reasoning Vision-Language Models (VLMs) have shown promising performance on
complex multimodal tasks. However, they still face significant challenges: they
are highly sensitive to reasoning errors, require large volumes of annotated
data or accurate verifiers, and struggle to generalize beyond specific domains.
To address these limitations, we explore self-correction as a strategy to
enhance reasoning VLMs. We first conduct an in-depth analysis of reasoning
VLMs' self-correction abilities and identify key gaps. Based on our findings,
we introduce Sherlock, a self-correction and self-improvement training
framework. Sherlock introduces a trajectory-level self-correction objective, a
preference data construction method based on visual perturbation, and a dynamic
beta for preference tuning. Once the model acquires self-correction
capabilities using only 20k randomly sampled annotated data, it continues to
self-improve without external supervision. Built on the Llama3.2-Vision-11B
model, Sherlock achieves remarkable results across eight benchmarks, reaching
an average accuracy of 64.1 with direct generation and 65.4 after
self-correction. It outperforms LLaVA-CoT (63.2), Mulberry (63.9), and
LlamaV-o1 (63.4) while using less than 20% of the annotated data.
6837ffdd1bfb4a669ad6de09
Yi Ding
claimed_verified
2025-05-29T07:41:19.811Z
662678dfdd43e904ef1dcd03
https://cdn-avatars.huggingface.co/v1/production/uploads/662678dfdd43e904ef1dcd03/9bbnrnsRV3ylSuZXwcSNv.jpeg
Yi Ding
user
Tuwhy
6837ffdd1bfb4a669ad6de0a
Ruqi Zhang
Sherlock: Self-Correcting Reasoning in Vision-Language Models
Reasoning Vision-Language Models (VLMs) have shown promising performance on
complex multimodal tasks. However, they still face significant challenges: they
are highly sensitive to reasoning errors, require large volumes of annotated
data or accurate verifiers, and struggle to generalize beyond specific domains.
To address these limitations, we explore self-correction as a strategy to
enhance reasoning VLMs. We first conduct an in-depth analysis of reasoning
VLMs' self-correction abilities and identify key gaps. Based on our findings,
we introduce Sherlock, a self-correction and self-improvement training
framework. Sherlock introduces a trajectory-level self-correction objective, a
preference data construction method based on visual perturbation, and a dynamic
beta for preference tuning. Once the model acquires self-correction
capabilities using only 20k randomly sampled annotated data, it continues to
self-improve without external supervision. Built on the Llama3.2-Vision-11B
model, Sherlock achieves remarkable results across eight benchmarks, reaching
an average accuracy of 64.1 with direct generation and 65.4 after
self-correction. It outperforms LLaVA-CoT (63.2), Mulberry (63.9), and
LlamaV-o1 (63.4) while using less than 20% of the annotated data.
Sherlock, a self-correction and self-improvement framework for reasoning vision-language models, enhances accuracy across benchmarks using limited annotated data.
vision-language models self-correction trajectory-level self-correction preference data visual perturbation dynamic beta Llama3.2-Vision-11B LLaVA-CoT Mulberry LlamaV-o1