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In Artificial Intelligence (AI), planning—the ability to formulate and execute a series of actions to achieve a specific outcome—has always been a significant challenge. Unlike simpler tasks such as text generation, planning requires step-by-step reasoning, where each decision must be based on previous steps to reach a final goal. While Large Language Models (LLMs) like GPT-4 have shown impressive capabilities in text generation and other areas, they struggle with planning tasks because they rely on pattern recognition and approximate retrieval rather than logical reasoning.

Recognizing this limitation, OpenAI introduced the Large Reasoning Model (LRM), known as o1 (Strawberry). The o1 model aims to address the shortcomings of traditional LLMs by integrating reasoning abilities that extend beyond simple text prediction.

Using PlanBench, a benchmark developed to evaluate AI planning skills, researchers tested o1’s ability to handle complex planning tasks. This article explores a research paper that evaluated o1’s potential, its performance, trade-offs, and limitations, bringing clarity to how AI is evolving in its approach to problem-solving.

What is PlanBench?

PlanBench is a benchmark designed to evaluate the planning and reasoning abilities of AI models. It consists of a series of planning tasks that test a model’s ability to generate step-by-step plans to achieve specific goals. Tasks range from simple to highly complex, and they are designed to simulate real-world problem-solving scenarios. By testing models like o1 on PlanBench, researchers can assess how well the model can reason through multiple steps, adjust to new information, and handle tasks without prior examples.

What is a Large Reasoning Model (LRM)?

Traditional LLMs are designed to generate coherent text by predicting the next word, but they lack the ability to reason through multi-step tasks. Large Reasoning Models (LRMs) aim to bridge this gap. Unlike LLMs, LRMs are trained to carry out approximate reasoning, making decisions based on logical sequences rather than surface-level associations.

The o1 model is one of the first examples of an LRM. It moves beyond LLMs like GPT-4 by incorporating two key techniques:

1. Chain-of-Thought (CoT) reasoning: This allows the model to break down complex problems into smaller, manageable steps. Think of it as showing your work in a math problem rather than just giving the final answer. 
2. Reinforcement learning (RL): This technique enables the model to learn from its own experiences, improving its decision-making over time. Similar to how humans learn through trial and error, o1 becomes better at solving problems the more it is trained.

Technical Details of o1

The architecture of o1 introduces a new approach to AI reasoning. Unlike previous LLMs, which generate responses by retrieving information based on surface-level patterns, o1 uses reinforcement learning to evaluate and refine its reasoning over time. During inference, o1 can dynamically adjust its reasoning paths, making it more adaptable to complex tasks. This dynamic inference allows the model to adjust its course based on feedback, improving its decision-making during execution.

For example, imagine you need to bake a cake, but you’re missing milk. The store is closed, and your neighbor might have some milk. A traditional LLM might struggle with this task and suggest using the ingredients you have without addressing the missing milk. In contrast, o1 would:

1. Identify the goal: Bake a cake.
2. List available ingredients: Flour, eggs, sugar.
3. Identify the missing ingredient: Milk.
4. Consider options: Store (closed), neighbor (potential source).
5. Plan action: Ask neighbor for milk.
6. If successful, proceed with baking; if not, consider alternatives (e.g., finding a recipe without milk).

Advantages and Challenges of o1

Advantages

1. High Accuracy on PlanBench: O1 achieved an outstanding 97.8% accuracy on Blocksworld¹ tasks in zero-shot settings, surpassing models like LLaMA 3.1 405B (which scored 62.6%). This demonstrates o1’s superior ability to reason through complex, multi-step tasks. Blocksworld, a classic AI problem involving rearranging stacks of blocks, is used to evaluate how well models can plan and execute tasks step-by-step. O1’s high performance in such tasks marks a significant advance in AI’s planning abilities.
2. Handling Complex and Obfuscated Tasks: One of o1’s standout features is its proficiency in solving Mystery Blocksworld² tasks, which are obfuscated versions of standard planning tests. Traditional LLMs often struggle to interpret abstracted problems, but o1 maintains much higher accuracy. This demonstrates o1’s ability to handle complexity and obfuscation better than other models. Its ability to decipher and solve more challenging, abstract versions of planning problems highlights its advanced reasoning capabilities and potential for real-world applications, where information is often incomplete or unclear.
3. Scalability: O1’s reasoning ability demonstrates impressive scalability. While other models often falter as task complexity increases, o1 maintains its effectiveness across a wide range of problem difficulties. Whether it’s a simple three-block task or a more intricate, multi-step problem involving up to 20 steps, o1 adjusts and maintains reasoning accuracy. This scalability makes o1 a versatile tool, potentially suitable for a wide array of planning scenarios across different industries.

Challenges

1. Efficiency and Cost: Despite its impressive performance, o1 comes with significant challenges in terms of efficiency and cost. The model’s computational expense—$42.12 per 100 instances—is far higher than GPT-4’s $1.80 per 100 instances. This high operational cost could limit o1’s accessibility and practical use in large-scale projects, where organizations may struggle to justify the expense. This cost factor could restrict o1’s application to high-value use cases where the improved reasoning accuracy justifies the increased expense.
2. Speed Trade-offs: While o1 demonstrates improved speed compared to older models, it still lags behind classical solvers like Fast Downward in terms of efficiency. Fast Downward is a classical planning solver that completes planning tasks in as little as 0.265 seconds. By contrast, o1-preview takes over 40 seconds on average for the same tasks. This significant time difference highlights a clear trade-off between accuracy and speed. O1 offers superior reasoning power and accuracy, but at the cost of slower processing speeds, which could be critical in real-world applications where rapid decision-making is essential.
3. Struggles with Unsolvable Tasks: Despite its advanced planning capabilities, o1 shows a notable weakness in recognizing unsolvable tasks. In a controlled test, o1 incorrectly generated plans for unsolvable problems 54% of the time, leading to false positives. This shortcoming points to the need for further refinement, particularly in distinguishing between solvable and unsolvable tasks. The inability to recognize when a problem has no solution could lead to wasted computational resources and potentially misleading outputs in practical applications.
4. Transparency Issues: One of the biggest challenges facing o1 is its lack of transparency. OpenAI has kept the internal workings of o1 confidential, including its reasoning traces—the series of intermediate steps that lead to a final decision. This “black box” nature of the system makes it difficult to interpret why the model makes certain decisions. In industries like healthcare or finance, where explainability is crucial, this could be a significant barrier to adoption. Without the ability to audit o1’s decision-making process, it may be challenging to deploy the system in regulated environments or situations where accountability is paramount.

Evaluation and Performance

The evaluation results for o1 on PlanBench demonstrate its strong reasoning capabilities. In zero-shot tasks, where the model solves problems without prior examples, o1 achieved 97.8% accuracy, a significant improvement over GPT-4 and LLaMA 3.1. Its performance in Mystery Blocksworld further solidified its reputation, achieving 52.8% accuracy—substantially higher than GPT-4’s 35.5%.

While o1’s accuracy is impressive, its speed remains a concern. In tasks requiring complex reasoning, o1 is slower than classical solvers like Fast Downward, which can solve planning problems in 0.265 seconds. The trade-off between accuracy and speedbecomes crucial in practical applications where time is a constraint. Additionally, o1’s high computational costs may limit its scalability, particularly for organizations looking for cost-effective AI solutions.

Model	Accuracy (Zero-shot Blocksworld)	Average Time per Task
o1	97.8%	40.43 seconds
GPT-4	73.4%	18.72 seconds
LLaMA 3.1 405B	62.6%	15.20 seconds
Fast Downward	100%	0.265 seconds

However, proprietary solutions like o1 aren’t the only approaches advancing AI reasoning. In parallel, open-source initiatives like g1 are pushing the boundaries of AI reasoning through community-driven efforts.

Open Source Contributions with G1

g1 is an open-source project designed to replicate certain reasoning capabilities using the Llama-3.1 70b model running on Groq hardware. The project aims to create dynamic Chain-of-Thought (CoT) reasoning—a process that breaks down complex problems into multiple steps, allowing the model to reason through them systematically. Unlike typical LLMs, which may struggle with certain logic problems, g1 uses prompting strategies to improve accuracy in reasoning tasks. It has shown notable improvements in solving common LLM stumbling blocks like the “Strawberry problem,” achieving up to 70% accuracy on logic challenges.

As an open-source initiative, g1 invites the developer community to explore and extend its capabilities. The project showcases how prompt engineering alone—without extensive retraining—can enhance LLM reasoning. It allows developers to build upon the platform, improving the model’s ability to “think” through multiple methods for problem-solving, making it a valuable experiment in advancing LLM reasoning without proprietary restrictions​(GitHub)​

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Conclusion and Future Implications

The development of Large Reasoning Models (LRMs) like o1 is enhancing AI’s ability to handle complex planning and reasoning tasks. O1’s strong performance on benchmarks like PlanBench shows that AI models are improving in their capacity for multi-step reasoning. However, challenges remain, such as high computational costs, slower processing speeds, and transparency issues, which could limit its adoption for broader, real-world applications.

In parallel to proprietary advancements, open-source projects like g1 are also contributing to AI progress. The g1 project, based on Llama-3.1 and Groq hardware, allows the community to explore methods like Chain-of-Thought (CoT)reasoning and prompt engineering. This open collaboration adds to the broader AI research landscape by enabling experimentation and refinement without the need for costly retraining, making it an important complement to proprietary models like o1.

Together, these proprietary and open-source efforts are helping push the boundaries of AI’s capabilities in planning and problem-solving, showing that progress in AI is being driven by both industry leaders, with proprietary models like o1, and the broader AI community through open-source contributions like g1.

1. Blocksworld is a classic problem used in AI research to evaluate how well models can plan and reason. In Blocksworld, the task is to rearrange stacks of blocks to match a specified goal configuration. The model must plan a series of actions such as picking up, stacking, or unstacking blocks to achieve the desired arrangement. It is often used as a benchmark to test AI models’ ability to plan and solve problems that require step-by-step reasoning. ↩︎
2. Mystery Blocksworld is a more challenging and abstract version of the standard Blocksworld problem. In this variation, some of the information about the initial configuration of blocks is hidden or obfuscated, making it harder for models to interpret the task. Models need to reason through the obfuscation, infer missing details, and still produce a valid plan. Solving Mystery Blocksworld requires deeper reasoning and problem-solving capabilities, making it a tough test for AI models like o1. ↩︎

Based on the Research paper, LLMS STILL CAN’T PLAN; CAN LRMS? A PRELIMINARY EVALUATION OF OPENAI’S O1 ON PLANBENCH by Karthik Valmeekam, Kaya Stechly, and Subbarao Kambhampati from Arizona State University.