Samuel Okafor

Same Luck, Different Choice: Twin-World Credit Assignment for Agentic Reinforcement Learning

Agentic reinforcement learning rewards a complete episode, yet the policy needs credit for every decision inside it. Counterfactual branching estimates this credit by re-running the episode after an alternative decision, but the branch draws its sampling noise independently of the factual episode, so the outcome differ...