titleReasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short
authorsHan Zhou, Adam X. Yang, Laurence Aitchison, Anna Korhonen, Albert Q. Jiang
date2026-06-08
venuearXiv (preprint, not peer reviewed)
idarXiv:2606.09380
affiliationMistral
upvotes10 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf
abstractReinforcement learning with verifiable rewards (RLVR) has become a leading paradigm for improving the reasoning ability of large language models through outcome-based supervision. However, verifiable rewards frequently become uninformative at the group level: when all sampled traces of a given prompt receive identical rewards, group-relative advantage estimation provides no gradient signal, even though the traces may differ substantially in reasoning quality. We propose Reasoning Arena, an adaptive training framework that routes such non-diverse reward groups to a judge system instead of discarding them. Beyond examining the final answer, Reasoning Arena constructs trace tournaments, where reasoning traces are compared head-to-head to expose finer-grained preferences within the group, converting reasoning quality into rich relative reward signals. To make reward estimation efficient, rather than exhaustively comparing every pair, each new trace is evaluated against a small, dynamically updated pool of previously generated traces as anchors to efficiently establish a relative ranking. We then fit a Bradley-Terry model on the incomplete comparison graph, enabling scalable RL integration without quadratic pairwise comparisons. Empirical results demonstrate that Reasoning Arena consistently outperforms the RLVR baseline by 7.6% on average in competition mathematics and coding benchmarks. By converting otherwise wasted zero-advantage samples into useful gradient updates, our method accelerates training by 27% to 41%, saving nearly 50% of generation compute, and substantially improves overall reasoning performance.

cite

@misc{zhou2026reasoning,
  title={Reasoning Arena: Trace Tournaments When Verifiable Rewards Fall Short},
  author={Han Zhou and Adam X. Yang and Laurence Aitchison and Anna Korhonen and Albert Q. Jiang},
  year={2026},
  eprint={2606.09380},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2606.09380}
}