Geometric Analysis of RLVR via Trainable Activation Vectors

This study identifies a low-dimensional effective manifold in activation space associated with reinforcement learning with verifiable rewards (RLVR). Findings show that the capacity required to reproduce RL gains is small but not infinitely compressible, with dimensionality constraints varying by injection depth.