This research paper evaluates the planning capabilities of Large Language Models (LLMs) and Large Reasoning Models (LRMs), focusing on OpenAI's o1 model, using the PlanBench benchmark, which features block-stacking problems. The study reveals that while LLMs have made limited progress in planning, o1 demonstrates significant improvement due to its approximate reasoning abilities as opposed to the approximate retrieval approach of LLMs. However, o1's performance isn't perfect, particularly with complex or longer problems, highlighting the need for further research in LRM architecture and evaluation methodologies. The study emphasizes that evaluating LRMs requires considering not only accuracy but also efficiency, cost, and guarantees, which are critical for practical applications.
Description: Table 2 compares the performance (accuracy and time) of o1, other LLMs, and a classical planner (Fast Downward) on various Blocksworld tasks. It shows that while o1 outperforms LLMs, it's not as fast or accurate as Fast Downward, highlighting the trade-off between general-purpose models and specialized systems.
Relevance: This table underscores the performance gap between LRMs and dedicated planning systems, highlighting the need for further research to bridge this gap and improve LRM efficiency.
Description: Figure 3 depicts the decrease in o1's accuracy as the length of the required plan increases in Blocksworld. It visually demonstrates the model's limitations in handling more complex problems.
Relevance: This figure visually reinforces the key finding that o1's reasoning abilities struggle with increased problem complexity, highlighting a crucial area for future development.
This research paper demonstrates that while Large Reasoning Models (LRMs) like o1 represent a significant advancement over LLMs in planning tasks, challenges remain, particularly in handling complex problems and unsolvable instances. o1's improved performance comes at a higher computational cost, emphasizing the need to consider efficiency alongside accuracy in practical applications. Future research should focus on developing more robust LRM architectures that can efficiently handle longer plans and unsolvable problems, exploring hybrid approaches that combine the strengths of LLMs/LRMs with dedicated solvers, and developing more comprehensive evaluation methods that consider accuracy, efficiency, and guarantees. Furthermore, a deeper understanding of LRM reasoning processes and error analysis will be crucial for refining these models and unlocking their full potential in real-world planning scenarios.
This abstract introduces a study evaluating the planning abilities of Large Language Models (LLMs) and Large Reasoning Models (LRMs), particularly OpenAI's o1 model, using the PlanBench benchmark. It highlights the slow progress of LLMs in planning tasks and suggests that o1, while significantly improved, still has limitations in terms of accuracy, efficiency, and guarantees.
The abstract effectively establishes the importance of planning in AI and the need to evaluate new models like o1.
The abstract succinctly presents the key findings, highlighting both o1's improvements and its remaining limitations.
While the abstract mentions the need for new metrics, it could briefly elaborate on what these might be.
Rationale: Providing more specific examples of new metrics would strengthen the abstract's call for future research.
Implementation: Mention specific metrics like planning time, cost per plan, or robustness to variations in problem descriptions.
The abstract uses qualitative terms like "quantum improvement." Adding some quantitative data would make the improvement more concrete.
Rationale: Quantifying the improvement would give readers a better understanding of the magnitude of o1's advancement.
Implementation: Include a brief statement like "o1 achieved X% accuracy compared to the previous best of Y%.
This introduction sets the stage for evaluating Large Reasoning Models (LRMs), specifically OpenAI's o1, using the PlanBench benchmark. It emphasizes the shift from approximate retrieval in LLMs to approximate reasoning in LRMs, highlighting o1's potential and the need for new evaluation methods.
The introduction clearly differentiates between LLMs and LRMs, highlighting the shift from retrieval to reasoning.
The introduction effectively justifies the need for new evaluation methods for LRMs by emphasizing their unique characteristics.
While the introduction states that o1 is an approximate reasoner, providing specific examples of its reasoning process would strengthen the argument.
Rationale: Concrete examples would make the distinction between LLMs and LRMs more tangible and persuasive.
Implementation: Include a brief example of how o1 approaches a planning problem differently from an LLM, perhaps using a simplified block-stacking scenario.
The introduction mentions extending PlanBench but could briefly elaborate on potential directions for this extension.
Rationale: Providing specific directions for extending PlanBench would further motivate the research and provide a roadmap for future work.
Implementation: Suggest specific extensions like incorporating more complex planning domains, varying problem sizes, or introducing uncertainty into the environment.
This section examines the performance of existing Large Language Models (LLMs) on the PlanBench benchmark, particularly focusing on block-stacking problems. It finds that even the most advanced LLMs struggle with these planning tasks, especially variations like "Mystery Blocksworld," suggesting their limitations in reasoning and planning compared to retrieval tasks.
The section provides a clear comparison of different LLMs on various Blocksworld tasks, allowing for a direct assessment of their capabilities.
The section effectively highlights the limitations of LLMs in planning, emphasizing the gap between their performance and the desired level of competence.
The section notes that one-shot prompting can worsen performance but doesn't delve into the reasons behind this.
Rationale: Understanding why one-shot prompting sometimes fails could lead to better prompting strategies or model improvements.
Implementation: Analyze the specific cases where one-shot prompting performs worse, looking for patterns in the prompts or the model's responses.
The Mystery Blocksworld obfuscation might be too brittle or easily reversed by LLMs.
Rationale: Different obfuscation techniques could provide a more robust test of semantic understanding and reasoning.
Implementation: Experiment with obfuscations that involve more complex semantic transformations or require more inferential steps to reverse.
Table 1 compares the performance of various Large Language Models (LLMs) on two block-stacking tasks: 'Blocksworld' and 'Mystery Blocksworld'. 'Blocksworld' involves arranging blocks according to clear instructions, while 'Mystery Blocksworld' uses confusing language for the same task. The table shows how many out of 600 problems each LLM solved correctly in both zero-shot (no example) and one-shot (one example provided) scenarios. The models are grouped by family (Claude, GPT, LLaMA, Gemini).
Text: "Table 1: Performance on 600 instances from the Blocksworld and Mystery Blocksworld domains across large language models from different families, using both zero-shot and one-shot prompts. Best-in-class accuracies are bolded."
Context: This table appears in the 'State-of-the-Art LLMs Still Can't Plan' section on page 2. It presents the performance of various LLMs on the Blocksworld and Mystery Blocksworld tasks, which serves as a baseline for comparison with the newer LRM models discussed later.
Relevance: This table is crucial because it demonstrates that even the most advanced LLMs struggle with planning tasks, especially when the instructions are unclear. This highlights the need for models with better reasoning abilities, like the LRMs discussed in the paper.
This section discusses the shift from approximate retrieval (like looking up answers in a vast library) in Large Language Models (LLMs) to approximate reasoning (like figuring things out step-by-step) in Large Reasoning Models (LRMs), using OpenAI's o1 as a prime example. It evaluates o1's performance on PlanBench, a benchmark for planning tasks, and finds that while o1 shows significant improvement over LLMs, its performance isn't perfect and degrades with more complex problems.
The section effectively explains why traditional LLMs struggle with planning tasks, using the analogy of approximate retrieval to illustrate their limitations.
The section provides a comprehensive analysis of o1's performance on various PlanBench tasks, including different problem sizes and unsolvable instances.
While the section speculates about o1's architecture, a deeper investigation of its internal reasoning process would be valuable.
Rationale: Understanding how o1 reasons would help identify its strengths and weaknesses, leading to better model development.
Implementation: Analyze the intermediate steps or reasoning traces (if accessible) to understand how o1 arrives at its solutions or identifies unsolvable problems.
Comparing o1's performance with other reasoning methods, such as symbolic planners or hybrid approaches, would provide a broader context.
Rationale: This comparison would help assess the relative advantages and disadvantages of different reasoning approaches for planning tasks.
Implementation: Evaluate the performance of symbolic planners or LLM-Modulo systems on the same PlanBench tasks used to evaluate o1, and compare their accuracy, efficiency, and robustness.
Figure 1 shows two line graphs comparing the performance of different models on the 'Mystery Blocksworld' task. One graph represents 'zero-shot' performance (no prior example given), and the other shows 'one-shot' performance (one example provided). The x-axis of each graph represents the length of the solution plan (number of steps), and the y-axis represents the percentage of problems solved correctly. Each line on the graphs corresponds to a different model, and you can see how their accuracy changes as the plans get longer. Generally, the lines slope downwards, meaning accuracy drops as the problems get harder (longer plans needed).
Text: "Figure 1: These examples are on Mystery Blocksworld. Fast Downward, a domain-independent planner [8] solves all given instances near-instantly with guaranteed perfect accuracy. LLMs struggle on even the smallest instances. The two LRMs we tested, o1-preview and o1-mini, are surprisingly effective, but this performance is still not robust, and degrades quickly with length."
Context: This figure is introduced at the beginning of the 'From Approximate Retrieval to Approximate Reasoning: Evaluating o1' section on page 3. It visually demonstrates the performance difference between LLMs, LRMs (o1-preview and o1-mini), and a classical planner (Fast Downward) on the Mystery Blocksworld task, highlighting the relative effectiveness of the LRMs compared to LLMs and their limitations compared to Fast Downward.
Relevance: This figure is important because it visually shows that the new 'reasoning' models (LRMs) perform much better than standard language models (LLMs) on the tricky 'Mystery Blocksworld' task. It also shows that even these new models aren't perfect and struggle with longer, more complex problems. This supports the idea that while LRMs are a step forward, there's still a lot of room for improvement in planning and reasoning.
Table 2 shows how well different models perform on several variations of the 'Blocksworld' problem, including a tricky version called 'Mystery Blocksworld' and an even trickier 'Randomized Mystery Blocksworld'. It compares the new 'reasoning' models (o1-preview and o1-mini) with a traditional planning system called 'Fast Downward'. The table shows how many out of 600 problems each model solved correctly and the average time it took them. 'Fast Downward' solves all the problems perfectly and very quickly. The o1 models do well on the regular 'Blocksworld' but struggle more with the 'Mystery' versions, taking much longer to answer.
Text: "Table 2: Performance and average time taken on 600 instances from the Blocksworld, Mystery Blocksworld and Randomized Mystery Blocksworld domains by OpenAI's ol family of large reasoning models and Fast Downward"
Context: This table, appearing on page 3, follows Figure 1 and provides a more detailed breakdown of the performance of the o1 models and Fast Downward on different Blocksworld variations, including accuracy and average time taken. It further emphasizes the performance gap between LRMs and a dedicated planner.
Relevance: This table is important because it provides a direct comparison between the new LRMs, older LLMs (indirectly through comparison with Fast Downward), and a dedicated planning system. It shows that while LRMs are much better than LLMs, they are still not as good or as fast as specialized tools. This highlights the trade-offs between general-purpose language models and specialized systems.
Figure 2 presents two graphs illustrating the relationship between plan length and the average number of reasoning tokens used by the o1-preview model. Graph (a) shows this relationship for 'Mystery Blocksworld', where obfuscated language is used to describe the block stacking task. Graph (b) shows the same relationship for the standard 'Blocksworld' task with clear instructions. Both graphs have 'Plan Length' on the x-axis and 'Average Reasoning Tokens' on the y-axis.
Text: "Figure 2"
Context: This figure is introduced on page 4, in the section discussing the shift from approximate retrieval to approximate reasoning. It is used to illustrate how o1-preview's resource usage (reasoning tokens) changes with the complexity of the planning problem.
Relevance: Figure 2 helps to understand how o1-preview's reasoning process scales with problem complexity. It shows whether the model's resource consumption increases proportionally with the difficulty of the task, which is important for evaluating its efficiency.
Figure 3, as visible (it is partially out of frame in the provided document), appears to be a line graph showing how the accuracy of different models changes as the length of the plan (number of steps) increases. The x-axis represents 'Plan Length', and the y-axis represents '% correct'. Multiple lines, likely representing different models, are plotted on the graph, showing a general trend of decreasing accuracy with increasing plan length.
Text: "Figure 3: All models are evaluated on Blocksworld. ol-preview outperforms the other LLMs, but its performance degrades more quickly with length."
Context: This figure, first mentioned on page 4, is part of the discussion on evaluating o1 and its performance on the Blocksworld task, especially as the problem size increases. It is presented after the discussion of o1's performance on the original test set and before the analysis of unsolvable instances.
Relevance: This figure is important because it shows how well o1-preview scales to more complex planning problems compared to other LLMs. The ability to handle longer plans is a key indicator of a model's reasoning capabilities.
Figure 3 is a line graph showing how the accuracy of the o1-preview model changes as the problems it tries to solve get more complex. The x-axis represents the number of steps needed to solve a problem (like the number of moves to stack blocks a certain way), ranging from 20 to 40. The y-axis represents how often the model gets the right answer (percentage correct), from 0% to 100%. There are multiple lines on the graph, likely comparing o1-preview's performance to other models or ideal solutions. The graph likely shows that as the number of steps increases, o1-preview's accuracy goes down.
Text: "Figure 3: Extending even the (regular, not obfuscated) Blocksworld dataset to problems requiring greater numbers of steps worsens the performance of o1-preview. When tested on 110 instances which each require at least 20 steps to solve, it only manages 23.63%."
Context: This figure is introduced on page 4 in the section 'From Approximate Retrieval to Approximate Reasoning: Evaluating o1'. It is presented to show how o1-preview's performance changes as the complexity of the Blocksworld problems increases.
Relevance: This figure is important because it shows that even though o1-preview is better than older models, it still has trouble with harder problems. This tells us that there's still room for improvement in how these models reason and plan.
Table 3 shows how well OpenAI's o1-preview model can tell if a block-stacking problem is impossible to solve. It looks at two types of problems: regular 'Blocksworld' and a more complicated version called 'Randomized Mystery Blocksworld'. The table tells us two things: 1. The 'True Negative' rate: how often the model correctly says a problem is impossible when it actually is. 2. The 'False Negative' rate: how often the model wrongly says a problem is impossible when it actually has a solution. Think of it like a medical test - you want it to correctly identify the sick people (true negative) and not misdiagnose healthy people as sick (false negative).
Text: "Table 3: Rate of claiming that a problem is impossible by OpenAI’s ol-preview on 100 unsolvable and 600 solvable instances in the Blocksworld and Randomized Mystery Blocksworld domains. The True Negative rate is the percent of unsolvable instances that were correctly marked as unsolvable. The False Negative rate is the percent of solvable instances that were incorrectly marked as unsolvable. Previous models are not shown in this table as their true negative and false negative rates were generally 0% across the board."
Context: This table appears on page 5, within the section discussing o1's evaluation. It follows the discussion of o1-preview's performance on larger Blocksworld problems and precedes the analysis of accuracy/cost tradeoffs.
Relevance: This table is important because it shows a new aspect of o1-preview's abilities - figuring out when a problem can't be solved at all. This is useful in real-world situations where knowing something is impossible is just as important as finding a solution.
This table presents the cost per 100 instances, measured in US dollars, for using different Large Language Models (LLMs) and Large Reasoning Models (LRMs). It separates the models into two categories: LLMs (like Claude, GPT variants, and Gemini) and LRMs (specifically o1-preview and o1-mini).
Text: "Table 4: Cost per 100 instances (in USD). LRMs are significantly more expensive than LLMs."
Context: This table is presented on page 6, within the discussion of accuracy/cost tradeoffs and guarantees for LRMs. It follows the analysis of o1's performance and precedes a comparison with classical planners and LLM-Modulo systems.
Relevance: This table is highly relevant because it directly addresses the cost implications of using LRMs for planning tasks. It highlights the significant cost difference between LLMs and LRMs, which is a crucial factor to consider when evaluating their practical applicability.
This conclusion summarizes the findings of the study, highlighting the improved performance of Large Reasoning Models (LRMs like OpenAI's o1) compared to traditional LLMs on planning tasks using the PlanBench benchmark. While LLMs showed some progress on basic Blocksworld problems, they struggled with more complex or obfuscated versions. LRMs, particularly o1, demonstrated significantly better accuracy but still faced limitations with longer problems and unsolvable instances. The conclusion also emphasizes the importance of considering accuracy/efficiency trade-offs and the lack of correctness guarantees with LRMs, suggesting alternative approaches like LLM-Modulo systems or dedicated solvers for certain applications.
The conclusion effectively summarizes the key findings of the study, highlighting the relative strengths and weaknesses of LLMs and LRMs.
The conclusion goes beyond simply reporting accuracy and discusses important practical considerations like efficiency, cost, and guarantees.
While the conclusion briefly mentions future evaluations, it could expand on specific research directions to address the identified limitations.
Rationale: Providing more concrete future research directions would strengthen the conclusion's impact and guide further work in the field.
Implementation: Suggest specific research areas like developing new LRM architectures that address the limitations with longer problems and unsolvable instances, or exploring hybrid approaches that combine the strengths of LLMs/LRMs with dedicated solvers.
The conclusion notes o1's limitations but could provide a deeper analysis of the types of errors it makes and their potential causes.
Rationale: A more detailed error analysis would provide valuable insights into the nature of LRM reasoning and guide future model improvements.
Implementation: Categorize the errors made by o1, such as incorrect action sequences, failure to recognize unsolvability, or exceeding computational limits. Investigate whether these errors are due to limitations in the model's architecture, training data, or reasoning process.