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🗣️🗺️🤖⚙️ Reasoning with Language Model is Planning with World Model

🤖 AI Summary

This paper outlines a new framework, Reasoning via Planning (RAP). It argues that 🤖🦜 Large Language Models (LLMs) sometimes 🤯 struggle with problems that are easy for humans, such as generating action plans, complex math, or logical reasoning.

  • This 😢 deficiency stems from the fact that LLMs lack an internal world model to predict the world state and simulate long-term outcomes.
  • The 💡 solution proposed is a new LLM reasoning framework called Reasoning via Planning (RAP).
  • RAP repurposes the 🤖 LLM as both a world model and a reasoning agent.
  • The framework incorporates a principled planning algorithm based on Monte Carlo Tree Search for 🗺️ strategic exploration of the reasoning space.
  • The paper 📈 demonstrates RAP’s superiority over strong baselines on challenging reasoning problems, including plan generation, math reasoning, and logical inference.
  • In one plan generation setting, RAP with LLaMA-33B even 👑 surpasses CoT with GPT-4, achieving a 33% relative improvement.

🤔 Evaluation

The paper 🧐 contrasts the new framework with existing methods, primarily Chain-of-Thought (CoT), arguing that current LLM reasoning is “instinctively” autoregressive, which is in stark contrast to the deliberate planning enabled by RAP. RAP’s approach formally introduces a world model, reward mechanisms, and state into a unified framework, which the authors claim other search-guided methods lack. For further understanding, the paper suggests a few areas to explore. It would be interesting to see how 🛠️ fine-tuning LLMs could enhance their reasoning and world model capabilities. Additionally, combining RAP with 🤝 external tools is an identified path for solving more complex real-world problems. The paper also highlights that the combination of multiple rewards improves performance, but the specific effects depend on the nature of the task.

📚 Book Recommendations

  • 🤖🧠 Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig: A foundational 🎓 text on planning algorithms and intelligent agents, relevant to the RAP framework.
  • 🤔🐇🐢 Thinking, Fast and Slow by Daniel Kahneman: Explores human thought systems—intuitive (fast) and deliberate (slow)—offering a 🧠 contrast to the paper’s comparison of LLM reasoning.
  • 🤖➕🧠➡️ Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto: A classic text on reinforcement learning, providing the theoretical underpinnings for the reward-based planning and 🎯 strategic exploration used in RAP.
  • The Alignment Problem by Brian Christian: Addresses the critical question of how to ensure machine learning systems ⚖️ align with human values.
  • Build a Large Language Model (From Scratch) by Sebastian Raschka: A hands-on guide for those who want to 🛠️ build a large language model from the ground up.
  • AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee: Offers a broader geopolitical 🌎 perspective on the global competition in artificial intelligence.
  • Multi-Agent Reinforcement Learning: Foundations and Modern Approaches by Stefano V. Albrecht, Filippos Christianos, and Lukas Schäfer: Dives into how multiple intelligent agents can 🤝 interact and learn in shared environments.

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