The Majority is not always right: RL training for solution aggregation

★

☆

SHARE

Overview

Paper Summary

Conflicts of Interest

Identified Weaknesses

Rating Explanation

Good to know

Topic Hierarchy

File Information

Paper Summary

Paperzilla title

AI Aggregator Learns to Outsmart Majority Voting in Math Problems

This paper introduces AggLM, an AI model trained to combine multiple solution attempts to math problems, outperforming simple majority voting and achieving a 50% accuracy on AIME25. It uses reinforcement learning from verifiable rewards, learning to synthesize correct answers even when they don't appear in the initial solution set.

Possible Conflicts of Interest

The authors are affiliated with Meta/FAIR, which may have an interest in developing advanced AI models.

Identified Weaknesses

Limited dataset diversity

The model is trained and evaluated on a small set of math competition problems. It's unclear how well it generalizes to other math domains or real-world problem-solving scenarios.

Dependence on base LLM

AggLM relies on the output of a base language model for generating initial solutions. Its performance is therefore tied to the quality and diversity of these initial solutions.

Computational cost

While more token-efficient than naive majority voting with many samples, AggLM still requires generating and processing multiple solutions, adding computational overhead.

Rating Explanation

This paper presents a novel approach to solution aggregation using reinforcement learning, demonstrating significant improvements over existing methods. The evaluation is rigorous and the ablation studies provide valuable insights. However, limited dataset diversity and reliance on a base LLM are notable limitations.

Good to know

This is our free standard analysis. Paperzilla Pro fact-checks every citation, researches author backgrounds and funding sources, and uses advanced AI reasoning for more thorough insights.

Explore Pro →