Co-RL trains reasoning with peer rewards and argues diversity is what prevents collapse.

Multiple independent models train against each other with peer-derived rewards and no ground-truth labels, gaining 3.0-8.6% across seven text benchmarks and 2.3-7.2% across four multimodal ones. The mechanism claim matters more than the numbers: varying architectures, model sizes and rephrased training samples across the cohort is what breaks the correlated errors that drive self-reinforcing collapse. (arXiv 2608.17253) Direct evidence that identical verifiers in a multi-agent check are worse than deliberately heterogeneous ones. If your two-model review setup runs the same model twice, that's not redundancy.