The Majority is not always right: RL training for solution aggregation
Overview
Paper Summary
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.
Explain Like I'm Five
Imagine a robot judge for math contests. It reads several student answers, figures out which parts are right, and puts them together to get the correct final answer, even if no single student got it completely right.
Possible Conflicts of Interest
The authors are affiliated with Meta/FAIR, which may have an interest in developing advanced AI models.
Identified Limitations
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.
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