arXiv AI August 13, 2026 By Kai Yang, Jingwei Xu, Wanyu Wang, Kai-Yuan Guo, Zhenbo Yu, Yi Wang, Yu Qiao

GCPO: Diagnosing and Constraining Subspace Geometry in Rollout RL for LLMs

arXiv:2608. 11674v1 Announce Type: cross Abstract: On-policy rollout methods such as GRPO are central to post-training of large language models, yet they frequently suffer from training instabilities, cross-task capability degradation, and response-length inflation.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Aug 3

GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR

arXiv:2601. 09361v4 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) is a key paradigm for improving large-scale reasoning models.

By Jiaying Zhang, Lei Shi, Jiguo Li, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He

llms reinforcement-learning fine-tuning

arXiv AI

Aug 7

All-Quadrant Bounded Clipping GRPO: Closing the Unbounded Blind Spot for Stable and Generalizable Training

arXiv:2601. 03895v2 Announce Type: replace-cross Abstract: Group Relative Policy Optimization (GRPO) has emerged as a popular algorithm for reinforcement learning with large language models (LLMs).

By Chi Liu, Xin Chen

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 5

SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter

arXiv:2606. 05800v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) often adopts GRPO-style group-relative updates, sampling multiple rollouts per prompt to construct normalized learning signals.

By Powei Chang, Jinpeng Zhang, Chaoqun Sun, MiniWell Tsao, Lianrui Li, Jianxiang Xiang, Chenyu Wang, Yukang Gao, Dongying Kong

reinforcement-learning benchmarks

arXiv Machine Learning

Aug 4

Reusing Rollouts under Policy Lag: Prefix-Normalized Policy Optimization for LLM Reinforcement Learning

arXiv:2608. 01418v1 Announce Type: cross Abstract: Autoregressive rollout generation is a major computational cost in reinforcement learning for large language models.

By Wenhao Zhang, Yibo Xie, Rui Wang, Jiahua Yang, Lei Jiang, Zibo Yang, Yawei Wang, Jiali Xu, jasperawang, Haoyang Long, Huan Xiong, alantzhao

llms reinforcement-learning benchmarks

arXiv AI

Jun 9

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models

arXiv:2606. 08446v1 Announce Type: cross Abstract: Despite being powerful, reinforcement learning with verifiable rewards (RLVR) induces extremely long COT, making it computationally expensive.

By Yang Zhou, Ranajoy Sadhukhan, Zhaofeng Sun, Zhuoming Chen, Souvik Kundu, Saket Dingliwal, Sai Muralidhar Jayanthi, Aram Galstyan, Haizhong Zheng, Beidi Chen

llms reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Aug 4

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training

arXiv:2608. 02391v1 Announce Type: cross Abstract: Tool-using large language model (LLM) agents produce long, multi-turn trajectories, making gradient-based post-training memory-intensive.

By Zhiyuan Wang, Shengcai Liu, Jiahao Wu, Ning Lu, Hui Ouyang, Shaofeng Zhang, Haoze Lv, Ke Tang

llms agents reinforcement-learning fine-tuning benchmarks

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GeoRA: Geometry-Aware Low-Rank Adaptation for RLVR

All-Quadrant Bounded Clipping GRPO: Closing the Unbounded Blind Spot for Stable and Generalizable Training

SALT: When More Rollouts Don't Help in Group-Based Policy Optimization and How to Make Them Matter

Reusing Rollouts under Policy Lag: Prefix-Normalized Policy Optimization for LLM Reinforcement Learning

Sparrow: Sparse Rollout for Stable and Efficient Long-context RL of Large Language Models

Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Training