The Majority is not always right: RL training for solution aggregation
Authors: name: Wenting Zhao, name: Pranjal Aggarwal, name: Swarnadeep Saha, name: Asli Celikyilmaz, name: Jason Weston, name: Ilia Kulikov

Scaling up test-time compute, by generating multiple independent solutions
and selecting or aggregating among them, has become a central paradigm for
improving large language models (LLMs) on challenging reasoning tasks. While
most prior work relies on simple majority voting or reward model ranking to
aggregate solutions, these approaches may only yield limited benefits. In this
work, we propose to learn aggregation as an explicit reasoning skill: given a
set of candidate solutions, we train an aggregator model to review, reconcile,
and synthesize a final, correct answer using reinforcement learning from
verifiable rewards. A key ingredient is careful balancing of easy and hard
training examples, allowing the model to learn both to recover
minority-but-correct answers as well as easy majority-correct answers.
Empirically, we find our method, AggLM, outperforms both strong rule-based and
reward-model baselines, across multiple benchmarks. Furthermore, it generalizes
effectively to solutions from differing models, including stronger ones than
contained in the training data, all while requiring substantially fewer tokens
than majority voting with larger numbers of solutions.