DPO Meets PPO: Reinforced Token Optimization for RLHF

TL;DR

This paper introduces Reinforced Token Optimization (RTO), a new framework that models RLHF as an MDP to learn token-wise rewards, improving policy training for language models over traditional PPO methods.

Contribution

The paper proposes RTO, combining DPO and PPO, to learn token-wise rewards from preference data, enabling more efficient and fine-grained policy optimization in RLHF.

Findings

RTO outperforms PPO by 7.5 points on AlpacaEval 2.

RTO outperforms other preference learning algorithms.

Theoretically, RTO finds near-optimal policies sample-efficiently.

Abstract

In the classical Reinforcement Learning from Human Feedback (RLHF) framework, Proximal Policy Optimization (PPO) is employed to learn from sparse, sentence-level rewards -- a challenging scenario in traditional deep reinforcement learning. Despite the great successes of PPO in the alignment of large language models, its open-source implementation is still largely sub-optimal. To address these issues, we introduce a framework that models RLHF problems as a Markov decision process (MDP), enabling the capture of fine-grained token-wise information. Under this framework, we introduce an algorithm Reinforced Token Optimization (\texttt{RTO}), which learns the token-wise reward function from preference data and performs policy optimization based on this learned token-wise reward signal. Theoretically, \texttt{RTO} is proven to have the capability of finding the near-optimal policy sample-efficiently. For its practical implementation, \texttt{RTO} innovatively integrates Direct Preference Optimization (DPO) and PPO. DPO, originally derived from sparse sentence rewards, surprisingly provides us with a token-wise characterization of response quality, which is seamlessly incorporated into our subsequent PPO training stage. Extensive experiments demonstrate that \texttt{RTO} performs better than PPO and other direct preference learning algorithms. In particular, RTO outperforms PPO by 7.5 points on the AlpacaEval 2 benchmark and by 4.1 points on Arena-Hard. Our code and models are available at \href{https://github.com/zkshan2002/RTO}{https://github.com/zkshan2002/RTO}.