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

Overview

The paper presents Adaptive Branching Monte Carlo Tree Search (AB-MCTS), a novel framework for improving inference-time computation in large language models (LLMs). This method leverages external feedback to dynamically decide whether to expand new candidate responses or revisit existing ones, enhancing reasoning capabilities.

Key Insights

• AB-MCTS Framework: Introduces a method that generalizes repeated sampling with multi-turn exploration and exploitation.
  • Evidence: Empirical results show AB-MCTS outperforms repeated sampling and standard MCTS.
  • Verifiable: Yes, through empirical evaluation.
• Performance: Demonstrates superior performance in complex coding and engineering tasks.
  • Evidence: Evaluated using frontier models.
  • Verifiable: Yes, through task-specific benchmarks.

BFSI Relevance

• Why Relevant: Enhances decision-making processes in AI-driven financial services by improving model inference efficiency and accuracy.
• Primary Sector: Financial Services
• Subsectors: Asset Management, Risk Management
• Actionable Implications:
  • Implement AB-MCTS to improve AI model performance in financial analysis.
  • Use enhanced inference capabilities for better risk assessment and decision-making.