A Comparative Study on Reasoning Patterns of OpenAI's o1 Model
Abstract: Enabling Large Language Models (LLMs) to handle a wider range of complex tasks (e.g., coding, math) has drawn great attention from many researchers.
As LLMs continue to evolve, increasing the number of model parameters yields diminishing performance improvements and heavy computational costs.
Recently,
OpenAI's o1 model has shown that inference strategies (i.e., Test-time Compute methods) can also significantly enhance the reasoning capabilities of LLMs. However, the mechanisms behind these methods are still unexplored.
In our work, to investigate the reasoning patterns of o1, we compare o1 with existing Test-time Compute methods (BoN, Step-wise BoN, Agent Workflow, and Self-Refine) by using OpenAI's GPT-4o as a backbone on general reasoning benchmarks in three domains (i.e., math, code and commonsense reasoning).
Specifically,
first,
our experiments show that the o1 model has achieved the best performance on most datasets.
Second,
as for the methods of searching diverse responses (e.g., BoN), we find the reward models' capability and the search space both limit the upper boundary of these methods.
Third,
as for the methods that break the problem into many sub-problems, the Agent Workflow has achieved better performance than Step-wise BoN due to the domain-specific system prompt for planning better reasoning processes.
Fourth,
we summarize six reasoning patterns of o1, and provide a detailed analysis across different reasoning benchmarks.
Paper Type: Long
Research Area: Resources and Evaluation
Research Area Keywords: O1, Thinking Chain
Contribution Types: Model analysis & interpretability, NLP engineering experiment
Languages Studied: English
Submission Number: 1181
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