Summary of Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities Of Lrm O1, by Karthik Valmeekam et al.
Planning in Strawberry Fields: Evaluating and Improving the Planning and Scheduling Capabilities of LRM o1
by Karthik Valmeekam, Kaya Stechly, Atharva Gundawar, Subbarao Kambhampati
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates whether large language models (LLMs) can plan a course of action to achieve a desired state, a fundamental ability of intelligent agents. Despite the development of new private and open-source LLMs since GPT3, progress has been slow. OpenAI claims their recent o1 model is designed to overcome limitations of autoregressive LLMs, making it a Large Reasoning Model (LRM). This paper evaluates the planning capabilities of two LRMs on planning and scheduling benchmarks. Results show that while o1 improves over autoregressive LLMs, this comes at a high inference cost, without guarantees for generated outputs. The paper also proposes combining o1 models with external verifiers in an LRM-Modulo system, ensuring the correctness of combined output while improving performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are smart computers that can understand and generate human-like text. They’re like super-smart robots that can talk to us! But what if these robots could also plan ahead and make decisions? That’s what this research is about: figuring out if LLMs can really do that. Scientists from OpenAI made a special kind of LLM called o1, which they think is better at planning than other LLMs. They tested o1 on some hard tasks, like scheduling appointments or making decisions. While o1 did okay, it was still not perfect and took a lot of computer power to work well. To make things better, the scientists suggested combining o1 with other special tools that can double-check the robot’s decisions. This could make LLMs even more helpful in the future! |
Keywords
» Artificial intelligence » Autoregressive » Inference
