Tandem Reinforcement Learning with Verifiable Rewards

Reinforcement learning has dramatically improved LLM reasoning on tasks like competition math — but the resulting models often reason in ways that are difficult for weaker models or humans to follow, limiting their real-world utility. Tandem Reinforcement Learning (TRL) addresses this by co-training a strong "senior" model alongside a frozen "junior" model: both contribute to generating reasoning chains, and the senior is rewarded as a team with the junior. This nudges the senior to reason in ways the junior can understand and continue. Beyond math tutoring, TRL has implications for human-AI collaboration, multi-model pipelines, and building AI systems whose reasoning remains interpretable and handoff-compatible across capability levels. Authors: Difan Jiao, Raghav Singhal, Robert West, Ashton Anderson Paper: https://arxiv.org/abs/2606.28166v1