Yu-Wang Wang

Exploration via Activation-Space Steering for Reinforcement Learning with Verifiable Rewards

Reinforcement learning with verifiable rewards (RLVR) trains a language model by sampling responses, scoring them with an automatic verifier, and updating the policy from the resulting rewards. The policy therefore receives direct learning signals only from trajectories that appear in its rollouts. Yet repeated samplin...