Step-by-step visual reasoning that's both accurate and lightning-fast.
LlamaV-o1 introduces a comprehensive framework for step-by-step visual reasoning in LLMs, with a new benchmark, evaluation metric, and curriculum learning approach.
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https://arxiv.org/abs/2501.06186
🤔 Original Problem:
→ Current visual reasoning models lack systematic evaluation methods and struggle with step-by-step problem solving, leading to inconsistent and unreliable results.
→ Existing benchmarks focus mainly on final answers, ignoring the quality of intermediate reasoning steps.
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🔍 Solution in this Paper:
→ Introduces VRC-Bench, a benchmark with 8 categories and 4,173 reasoning steps for evaluating multi-step visual reasoning.
→ Implements a novel metric assessing reasoning quality at individual step level, focusing on correctness and logical coherence.
→ Develops LlamaV-o1, a multimodal model using curriculum learning and beam search for efficient inference.
→ Uses two-stage training: first for summarization and caption generation, then for detailed reasoning.
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💡 Key Insights:
→ Step-by-step reasoning improves model interpretability and accuracy
→ Curriculum learning helps models develop foundational skills before tackling complex tasks
→ Beam search optimization reduces computational complexity from O(n²) to O(n)
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📊 Results:
→ LlamaV-o1 achieves 67.3% average score across benchmarks, 3.8% higher than Llava-CoT
→ 5× faster inference scaling compared to existing methods
→ Strong performance in Math & Logic (83.18%), Scientific Reasoning (86.75%), OCR tasks (93.44%)
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