SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks

TL;DR

SPPO is a new scalable reinforcement learning algorithm that improves long-horizon reasoning in language models by combining sample efficiency with stable outcome-based updates, outperforming standard PPO.

Contribution

Introduces Sequence-Level PPO (SPPO), a novel method reformulating reasoning as a Contextual Bandit problem with a decoupled value function for better efficiency and stability.

Findings

SPPO outperforms standard PPO on mathematical benchmarks.

SPPO matches the performance of computationally intensive group-based methods.

SPPO offers a resource-efficient approach for reasoning LLMs.

Abstract

Proximal Policy Optimization (PPO) is central to aligning Large Language Models (LLMs) in reasoning tasks with verifiable rewards. However, standard token-level PPO struggles in this setting due to the instability of temporal credit assignment over long Chain-of-Thought (CoT) horizons and the prohibitive memory cost of the value model. While critic-free alternatives like GRPO mitigate these issues, they incur significant computational overhead by requiring multiple samples for baseline estimation, severely limiting training throughput. In this paper, we introduce Sequence-Level PPO (SPPO), a scalable algorithm that harmonizes the sample efficiency of PPO with the stability of outcome-based updates. SPPO reformulates the reasoning process as a Sequence-Level Contextual Bandit problem, employing a decoupled scalar value function to derive low-variance advantage signals without multi-sampling. Extensive experiments on mathematical benchmarks demonstrate that SPPO significantly surpasses standard PPO and matches the performance of computation-heavy group-based methods, offering a resource-efficient framework for aligning reasoning LLMs.