SSPO: Subsentence-level Policy Optimization

• URL: http://arxiv.org/abs/2511.04256v1
• Date: Thu, 06 Nov 2025 10:48:31 GMT
• Title: SSPO: Subsentence-level Policy Optimization
• Authors: Kun Yang, Zikang chen, Yanmeng Wang, Zhigen Li,
• Abstract summary: This paper introduces SSPO, which applies sentence-level importance ratio, taking the balance between GRPO and GSPO.<n>SSPO achieves an average score of 46.57 across five datasets, surpassing GRPO (43.01) and GSPO (44.42) and wins state-of-the-art performance on three datasets.
• Score: 11.74548168856559
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a significant part of post-training of the Large Language Models (LLMs), Reinforcement Learning from Verifiable Reward (RLVR) has greatly improved LLMs' reasoning skills. However, some RLVR algorithms, such as GRPO (Group Relative Policy Optimization) and GSPO (Group Sequence Policy Optimization), are observed to suffer from unstable policy updates and low usage of sampling data, respectively. The importance ratio of GRPO is calculated at the token level, which focuses more on optimizing a single token. This will be easily affected by outliers, leading to model training collapse. GSPO proposed the calculation of the response level importance ratio, which solves the problem of high variance and training noise accumulation in the calculation of the GRPO importance ratio. However, since all the response tokens share a common importance ratio, extreme values can easily raise or lower the overall mean, leading to the entire response being mistakenly discarded, resulting in a decrease in the utilization of sampled data. This paper introduces SSPO, which applies sentence-level importance ratio, taking the balance between GRPO and GSPO. SSPO not only avoids training collapse and high variance, but also prevents the whole response tokens from being abandoned by the clipping mechanism. Furthermore, we apply sentence entropy to PPO-CLIP to steadily adjust the clipping bounds, encouraging high-entropy tokens to explore and narrow the clipping range of low-entropy tokens. In particular, SSPO achieves an average score of 46.57 across five datasets, surpassing GRPO (43.01) and GSPO (44.42), and wins state-of-the-art performance on three datasets. These results highlight SSPO's effectiveness in leveraging generated data by taking the essence of GSPO but rejecting its shortcomings.

Related papers

• Outcome-Grounded Advantage Reshaping for Fine-Grained Credit Assignment in Mathematical Reasoning [60.00161035836637]
  Group Relative Policy Optimization has emerged as a promising critic-free reinforcement learning paradigm for reasoning tasks.<n>We introduce Outcome-grounded Advantage Reshaping (OAR), a fine-grained credit assignment mechanism that redistributes advantages based on how much each token influences the model's final answer.<n>OAR-G achieves comparable gains with negligible computational overhead, both significantly outperforming a strong GRPO baseline.
  arXiv  Detail & Related papers  (2026-01-12T10:48:02Z)
• Segmental Advantage Estimation: Enhancing PPO for Long-Context LLM Training [17.530233901658253]
  Segmental Advantage Estimation mitigates the bias that Generalized Advantage Estimation can incur in Reinforcement Learning with Verifiable Rewards.<n> SAE achieves superior performance, with marked improvements in final scores, stability, and sample efficiency.
  arXiv  Detail & Related papers  (2026-01-12T08:41:47Z)
• Token-Regulated Group Relative Policy Optimization for Stable Reinforcement Learning in Large Language Models [18.785063555637613]
  Group Relative Policy Optimization (GRPO) has demonstrated strong performance.<n>It suffers from a critical issue: low-probability tokens disproportionately dominate gradient updates.<n>This imbalance leads to unstable training and suppresses the contribution of high-probability tokens.
  arXiv  Detail & Related papers  (2025-10-29T08:07:47Z)
• Repurposing Synthetic Data for Fine-grained Search Agent Supervision [81.95597592711688]
  LLM-based search agents are increasingly trained on entity-centric synthetic data.<n> prevailing training methods discard this rich entity information, relying instead on sparse, outcome-based rewards.<n>We introduce Entity-aware Group Relative Policy Optimization (E-GRPO), a novel framework that formulates a dense entity-aware reward function.
  arXiv  Detail & Related papers  (2025-10-28T17:50:40Z)
• Single-stream Policy Optimization [21.214853668053234]
  We introduce Single-stream Policy Optimization (SPO)<n>SPO replaces per-group baselines with a persistent, KL-adaptive value tracker and normalizes advantages globally across the batch.<n>Being group-free, SPO enables higher throughput and scales effectively in long-horizon or tool-integrated settings.
  arXiv  Detail & Related papers  (2025-09-16T16:39:11Z)
• Geometric-Mean Policy Optimization [117.05113769757172]
  Group Relative Policy Optimization ( GRPO) has significantly enhanced the reasoning capability of large language models.<n> GRPO is observed to suffer from unstable policy updates when facing tokens with outlier importance-weighted rewards.<n>We propose Geometric-Mean Policy Optimization (GMPO) to improve the stability of GRPO through suppressing token reward outliers.
  arXiv  Detail & Related papers  (2025-07-28T09:54:05Z)
• On the Effect of Negative Gradient in Group Relative Deep Reinforcement Optimization [52.76330545825083]
  Reinforcement learning (RL) has become popular in enhancing the reasoning capabilities of large language models (LLMs)<n>We identify a previously unrecognized phenomenon we term Lazy Likelihood Displacement (LLD), wherein the likelihood of correct responses marginally increases or even decreases during training.<n>We develop a method called NTHR, which downweights penalties on tokens contributing to the LLD. Unlike prior DPO-based approaches, NTHR takes advantage of GRPO's group-based structure, using correct responses as anchors to identify influential tokens.
  arXiv  Detail & Related papers  (2025-05-24T18:58:51Z)
• DISCO Balances the Scales: Adaptive Domain- and Difficulty-Aware Reinforcement Learning on Imbalanced Data [65.09939942413651]
  We propose a principled extension to GRPO that addresses inter-group imbalance with two key innovations.<n> Domain-aware reward scaling counteracts frequency bias by reweighting optimization based on domain prevalence.<n>Difficulty-aware reward scaling leverages prompt-level self-consistency to identify and prioritize uncertain prompts that offer greater learning value.
  arXiv  Detail & Related papers  (2025-05-21T03:43:29Z)
• DisCO: Reinforcing Large Reasoning Models with Discriminative Constrained Optimization [50.91849555841057]
  Group Relative Policy Optimization is a reinforcement learning method for large reasoning models (LRMs)<n>We introduce a new Discriminative Constrained Optimization framework for reinforcing LRMs, grounded in the principle of discriminative learning.<n>DisCO significantly outperforms GRPO and its improved variants such as DAPO, achieving average gains of 7% over GRPO and 6% over DAPO.
  arXiv  Detail & Related papers  (2025-05-18T11:08:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.