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About this paper
Han Zhou, Adam X. Yang, Laurence Aitchison, Anna Korhonen, Albert Q. Jiang
Reasoning Arena improves reinforcement-learning training for reasoning models by retaining verifier rewards when candidate traces differ in correctness and using pairwise judge comparisons when all traces share the same outcome. In experiments with an 8B policy trained on STEM data, Reasoning Arena-Live raised the average from 46.3 to 53.9 and nearly halved per-step generations, while total compute remains unestablished.
Key insights
- Intelligently combining precise deterministic verifiers with nuanced LLM judges, applied adaptively, provides a more effective and efficient reward signal for training LLM reasoning than using either in isolation.
- Evaluating the quality of intermediate reasoning steps through pairwise comparisons, rather than just final outcomes or pointwise scoring, is crucial for extracting fine-grained learning signals that improve logical soundness.
- Making LLM-as-a-judge scalable through dynamic opponent selection in 'live tournaments' and robust reward aggregation via the Bradley-Terry model allows for practical integration into large-scale reinforcement learning pipelines.
Summary by alphaXiv.
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