Jackpot: A Winning Strategy for Efficient Reinforcement Learning with LLMs

This research introduces Jackpot, a novel framework designed to enhance the efficiency of Reinforcement Learning for Generative AI, especially for Large Language Models. By leveraging Optimal Budget Rejection Sampling, Jackpot promises to significantly reduce the computational cost associated with training these complex models, opening doors for broader applications.

• •Jackpot uses Optimal Budget Rejection Sampling to reduce the discrepancy between the rollout model and the evolving policy in Reinforcement Learning.
• •The framework includes a unified training objective that updates policy and rollout models simultaneously.
• •Empirical results show Jackpot improves training stability, achieving performance comparable to on-policy RL.