LLM Multi-Step Reasoning Stages with LlamaV-o1
The LlamaV-o1 model is designed to perform step-by-step visual reasoning internally, utilising a multi-step curriculum learning approach to decompose complex tasks into manageable sub-tasks.
Introduction
This internal processing enhances the model’s reasoning capabilities but can impact inference latency.
The sequential nature of multi-step reasoning requires additional computational resources and time, potentially leading to slower response times compared to models employing single-step reasoning.
Therefore, while LlamaV-o1’s approach improves reasoning accuracy and interpretability, it may result in increased inference latency, which is a trade-off to consider in time-sensitive applications.
Context & Reasoning
Large Language Models (LLMs) managing context internally involves maintaining a coherent understanding of ongoing dialogue or information across multiple inputs, ensuring responses are relevant and consistent.
Reasoning, on the other hand, refers to the model’s ability to process and analyse information logically to draw conclusions or solve problems.
Context management focuses on memory and relevance, while reasoning emphasises logical steps and problem-solving.
Effective context handling helps in sustaining conversation flow, while strong reasoning capabilities enable models to tackle complex, multi-step tasks.
Some Background
We have grown accustomed to using LLMs to understand our text input (Natural Language Understanding/NLU), generate text (Natural Language Generation/NLG).
Added to this translation of human language, language related tasks like summarisation, extracting key points or tasks and more.
Then we added visual capabilities to LLMs and called them Large Multimodal Models (LMMs) with extended capabilities to combine text, images / videos, allowing for more complex multimodal tasks like image captioning, visual question answering, and video analysis.
To effectively solve these tasks, visual reasoning is essential for LMMs to process and connect diverse information, ensuring logical coherence and sequential problem-solving. The ability to reason across multiple modalities is crucial to addressing complex real-world problems.
Most benchmarks focus primarily on end-task accuracy, neglecting the quality of intermediate reasoning steps.
Step By Step Reasoning
Large Language Models (LLMs) must be capable of handling complex compound queries and decomposing them into manageable sub-steps, much like humans do when solving intricate problems.
This ability allows the model to follow a logical order in addressing each component of the query, ensuring that the final answer is coherent and well-founded.
Decomposing complex queries enhances the model’s reasoning transparency, as each step can be inspected and validated.
This not only improves the reliability and interpretability of the model’s output but also provides insight into the model’s reasoning strength, demonstrating its capacity to process and integrate information systematically.
The figure above compares the reasoning abilities of LlamaV-o1 with the closed-source models Gemini-1.5-Flash and Claude-3.5-Sonnet on a pattern recognition task from VRC-Bench.
Claude-3.5-Sonnet concludes none of the options, but its reasoning doesn’t fully align with the observed logic (highlighted in red).
Gemini-1.5-Flash shows weaker reasoning with less logical coherence (also highlighted in red).
LlamaV-o1, however, provides clearer and more systematic reasoning, correctly identifying option D as following the pattern, demonstrating its strong logical reasoning capability.
Multi-Step Chain-of-Thought Reasoning
Multi-step chain-of-thought reasoning is essential for handling complex tasks that require sequential decision-making and logical coherence.
Unlike single-step reasoning, which often skips intermediate steps, multi-step reasoning enables models to break down problems into smaller, manageable parts, ensuring transparency and consistency.
This approach mirrors human problem-solving, where each step is systematically reasoned through. For example, answering a complex question about an image might involve identifying objects, understanding their relationships, and synthesizing this information to provide a coherent response.
By adopting multi-step reasoning, models become more interpretable and better aligned with human-like problem-solving, paving the way for more robust and versatile AI systems.
Chief Evangelist @ Kore.ai | I’m passionate about exploring the intersection of AI and language. From Language Models, AI Agents to Agentic Applications, Development Frameworks & Data-Centric Productivity Tools, I share insights and ideas on how these technologies are shaping the future.
