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Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers (Feb 2025)

Title: Multi-Agent Verification: Scaling Test-Time Compute with Multiple Verifiers (Feb 2025) Link: http://arxiv.org/abs/2502.20379v1 Date: February 2025 Summary: The paper introduces Multi-Agent Verification (MAV), a test-time compute paradigm that combines multiple verifiers, specifically Aspect Verifiers (AVs), to improve language model performance without additional training. The proposed method, BON-MAV, leverages best-of-n sampling with aspect verifiers and demonstrates improvements in accuracy, weak-to-strong generalization, and self-improvement across various tasks and language models. Key Topics: Multi-Agent Verification (MAV) Aspect Verifiers (AVs) Test-time compute Language models (LLMs) Best-of-n sampling Weak-to-strong generalization Self-improvement Verifier Engineering Chapters: 00:00:00 - Introduction to Multi-Agent Verification 00:00:13 - Tackling LLM Performance 00:00:25 - Optimizing LLM Evaluation 00:00:41 - Multi-Agent Verification (MAV) Explained 00:01:01 - Aspect Verifiers (AVs) 00:01:13 - Spreading Out the Workload 00:01:23 - Impressive Improvements with MAV 00:01:34 - Best-of-n Sampling 00:01:50 - Cooking Competition Analogy 00:02:05 - Bon Mav Testing and Results 00:02:22 - Weak AVs Improving Strong LLMs 00:02:37 - Counterintuitive Results 00:02:54 - Generalization Game Changer 00:03:02 - LLMs Improving Themselves 00:03:15 - Self Critique Boosts Performance 00:03:35 - How Aspect Verifiers Work 00:03:53 - Examples of Aspect Verifiers 00:04:13 - Harnessing Different Strengths 00:04:28 - Combining Verifier Outputs 00:04:43 - Simple Voting System 00:05:00 - Future Possibilities 00:05:18 - Beyond Basic Voting Systems 00:05:34 - Two Stage Voting 00:05:52 - AI Agents Debating 00:06:11 - Experiment Setups 00:06:30 - Math Data Set 00:06:58 - Bon Mav Results in Math 00:07:20 - Accuracy Improvements 00:07:43 - Smaller Models Gain More 00:08:00 - Scaling Compute 00:08:27 - Diminishing Returns 00:09:05 - Testing Beyond Math 00:09:21 - General Knowledge 00:09:38 - Bon Mav Results for General Knowledge 00:10:00 - External Feedback is Key 00:10:16 - Graduate Level Reasoning Problems 00:10:38 - Bon Mav Results are Interesting 00:10:54 - High-Level Problems 00:11:14 - Coding Challenges 00:11:37 - Human Eval Results 00:11:58 - Coding Requires Precision 00:12:09 - Weak to Strong Generalization 00:12:35 - Democratizing AI Development 00:12:49 - Self-Improvement 00:13:30 - LLMs Doing Their Own Peer Review 00:14:03 - Aspect Verifier Prompts 00:14:24 - Domain-Specific System Prompt 00:14:45 - Example Prompts 00:15:02 - Domain-Independent Verification Prompt 00:15:28 - Prompt Engineering 00:15:55 - Picking the Best Team 00:16:18 - Engineering Domain-Specific Teams 00:16:50 - Skipping Verifier Engineering 00:17:24 - Expertise Synergy 00:17:37 - Verifiers in Action 00:17:59 - Math Data Set Example 00:18:24 - Team of Detectives 00:19:07 - Different Base LLMs 00:19:31 - Element of Subjectivity 00:20:04 - Team of Experts 00:20:30 - Real-World Examples 00:20:41 - Boost Creativity and Innovation 00:21:06 - Helping Humans Achieve More 00:21:37 - Impressive Results 00:21:46 - Final Part 00:22:16 - Future of AI 00:22:55 - Proof of Concept 00:23:15 - AI Teams 00:23:26 - Challenges 00:23:40 - What is Intelligence? 00:24:15 - Cool Vision 00:24:31 - Key Takeaway 00:24:54 - Wrapping Up