Wider or Deeper? Scaling LLM Inference-Time Compute with Adaptive Branching Tree Search

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Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

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Paper Summary

Paperzilla title

Making LLMs Smarter at Inference Time: To Explore or Exploit, That Is the Question!

This paper introduces Adaptive Branching Monte Carlo Tree Search (AB-MCTS), a new method to improve the reasoning skills of Large Language Models (LLMs) during the "thinking" process. It helps LLMs figure out when to explore new ideas ("go wider") versus refine existing ones ("go deeper") based on feedback, leading to better performance on complex tasks like coding and machine learning.

Possible Conflicts of Interest

The authors are affiliated with Sakana AI, a company potentially invested in the development and application of LLMs, which may introduce a bias towards portraying the proposed method favorably.

Identified Weaknesses

Reliance on Score Evaluator

The effectiveness of AB-MCTS hinges on having a reliable score evaluator, which can be difficult to develop for certain complex tasks or real-world scenarios where the true evaluation metric is inaccessible during the search process.

Limited Evaluation on Real-World Datasets

While the benchmark results are promising, more extensive evaluation on diverse real-world datasets is needed to fully assess the generalizability and practical impact of AB-MCTS.

Computational Cost

The adaptive branching nature of AB-MCTS, while offering flexibility, can also increase computational costs compared to simpler methods like repeated sampling, particularly for tasks with high evaluation overhead like MLE-Bench.

Rating Explanation

The paper presents a novel and promising approach to enhancing LLM inference-time reasoning by introducing the concept of adaptive branching within a tree search framework. The empirical results across diverse benchmarks and with different LLM models demonstrate the effectiveness and robustness of AB-MCTS. However, limitations such as the reliance on a score evaluator and the computational cost warrant further investigation, preventing a top rating of 5.

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