title: Sherlock: Self-Correcting Reasoning in Vision-Language Models
summary: 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.
paper_authors._id: 6837ffdd1bfb4a669ad6de09
paper_authors.name: Yi Ding
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-29T07:41:19.811Z
paper_authors._id: 662678dfdd43e904ef1dcd03
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/662678dfdd43e904ef1dcd03/9bbnrnsRV3ylSuZXwcSNv.jpeg
paper_authors.fullname: Yi Ding
paper_authors.type: user
paper_authors.user: Tuwhy
paper_authors._id: 6837ffdd1bfb4a669ad6de0a
paper_authors.name: Ruqi Zhang
paper_title: Sherlock: Self-Correcting Reasoning in Vision-Language Models
paper_summary: 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.
paper_ai_summary: Sherlock, a self-correction and self-improvement framework for reasoning vision-language models, enhances accuracy across benchmarks using limited annotated data.
paper_ai_keywords: vision-language models self-correction trajectory-level self-correction preference data visual perturbation dynamic beta Llama3.2-Vision-11B LLaVA-CoT Mulberry LlamaV-o1