New Chain of Computation architecture boosts LLM planning capabilities

Researchers have developed a new computational architecture called Chain of Computation (COC) to improve the planning capabilities of Large Language Models (LLMs). This architecture integrates a transformer-based LM within an iterative loop, utilizing a Structured Context Window (SCW) that maintains a constant size while allowing selective access to information. Experiments show that even smaller LLMs trained with COC can achieve over 99.89% success rates on planning problems like BlocksWorld and the Pancake puzzle, generalizing from minimal training data. Further enhancements, such as symbolic arithmetic support or a deterministic pushdown automaton formulation for the SCW, enable COC to solve complex instances of the Tower of Hanoi problem with over a million actions. AI

IMPACT Enhances LLM performance on complex planning tasks, potentially enabling more sophisticated AI agents.

RANK_REASON The cluster contains an academic paper detailing a new method for improving LLM planning capabilities. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• BlocksWorld
• Chain of Computation
• Large Language Models
• Pancake puzzle
• pushdown automaton
• Structured Context Window
• Tower of Hanoi
• Transformers
• Turing Machine

• paper
• model release

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