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

• URL: http://arxiv.org/abs/2604.08865v1
• Date: Fri, 10 Apr 2026 01:58:21 GMT
• Title: SPPO: Sequence-Level PPO for Long-Horizon Reasoning Tasks
• Abstract summary: Sequence-Level PPO (SPPO) is a scalable algorithm that harmonizes the sampletextual efficiency of PPO with the stability of outcome-based updates.<n>SPPO significantly surpasses standard PPO and matches the performance of computation-heavy group-based methods.
• Score: 41.49967840381499
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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