Agon

Agon: Competitive cross-model RL uses rival models as implicit reasoning graders

Agon is a new reinforcement learning framework where two competing models grade each other implicitly by attempting the same problems in alternating roles — one drafts a solution, the other reads it while solving, and each is rewarded for out-solving the rival. This sidesteps the need for process labels or a reward model, and because both models are jointly optimized, each faces a progressively stronger opponent. On the hard split of DeepMath with Qwen3, Agon doubles GRPO's pass@1, roughly eight times the gain of an untrained Mixture-of-Agents baseline, with results replicating on competitive programming and across model families.