PPO's clip was only one geometry: swapping in KL divergence with atomic estimation lifts math average from 27.7 to 41.5
PPO's clipping in LLM reinforcement learning is generalized into a two-dimensional design space of divergence geometry and estimator topology, where KL divergence with atomic estimation raises average scores by about 50% on math and 14% on coding over standard GRPO across eight competition-level benchmarks.