PulseAugur
EN
LIVE 09:28:02

New SP3O method mitigates Value Flattening in PPO for LLMs

By PulseAugur Editorial · [2 sources] ·

Researchers have identified a failure mode in Proximal Policy Optimization (PPO) called Value Flattening, where state values estimated by a critic become flat despite sharp changes across intermediate states. This issue is exacerbated in larger state spaces and can lead to redundant updates. To address this, a new method called SParse Proximal Policy Optimization (SP$^3$O) is proposed, which applies value loss to only a few selected states per response. Experiments on Qwen3-Base demonstrate that SP$^3$O effectively mitigates Value Flattening and improves policy learning. AI

IMPACT Introduces a method to improve policy learning in large language models by addressing a specific critic learning failure.

RANK_REASON Academic paper detailing a new method for reinforcement learning.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New SP3O method mitigates Value Flattening in PPO for LLMs

How we ranked this

Signal score
2 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Research
Academic paper detailing a new method for reinforcement learning.
Source corroboration
2 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, model release
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
1 days old
Coverage has settled into its steady-state source set.

Full methodology in our editorial standards.

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yizhuo Li, Jianhao Yan, Yun Luo, Zhi Wang, Futing Wang, Rong-Xi Tan, Kanghui Tian, Ganqu Cui, Ning Ding, Peilin Zhao, Yafu Li, Yu Cheng ·

    Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening

    arXiv:2609.18708v1 Announce Type: cross Abstract: In reinforcement learning for large language models, Proximal Policy Optimization (PPO) commonly uses a critic to estimate state values and reduce the variance of policy updates. However, we uncover a systematic failure mode in PP…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening

    In reinforcement learning for large language models, Proximal Policy Optimization (PPO) commonly uses a critic to estimate state values and reduce the variance of policy updates. However, we uncover a systematic failure mode in PPO critics, which we call Value Flattening: state v…

RELATED ENTITIES

RELATED TOPICS