arXiv AI June 30, 2026 By Yupeng Chang, Yuan Wu, Yi Chang

BV-Blend: Uncertainty-Weighted Historical Baselines for Stable Critic-Free RL with Verifiable Rewards

arXiv:2606. 28707v1 Announce Type: new Abstract: Critic-free reinforcement learning with verifiable rewards (RLVR), exemplified by Group Relative Policy Optimization (GRPO), avoids training a value function (critic) and reduces memory and compute overhead relative to critic-based PPO pipelines for aligning large language models.

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

arXiv Machine Learning

Jun 9

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning

arXiv:2606. 08088v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has recently become a key paradigm for improving the reasoning abilities of Large Language Models (LLMs), yet it remains limited by sparse binary rewards and its ignorance of model-internal uncertainty.

By Qing Miao, Yiming Zhao, Jing Yang, Chenxi Liu, Yuehai Chen, Yuewen Liu, Shaoyi Du, Badong Chen

llms reinforcement-learning

arXiv Machine Learning

1d ago

Le Critique: Privileged Value Functions for LLM Reinforcement Learning

arXiv:2608. 16739v1 Announce Type: new Abstract: Reinforcement learning algorithms for Large Language Models (LLMs) are largely distinguished by their variance reduction strategy.

By Siddarth Venkatraman, Matthieu Dinot, Laurence Aitchison

llms reinforcement-learning efficiency

arXiv Machine Learning

Jun 2

Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation

arXiv:2605. 21125v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO), a prominent algorithm within the Reinforcement Learning from Verifiable Rewards (RLVR) framework, has achieved strong results in improving the reasoning capabilities of large language models (LLMs).

By Xixiang He, Qiyao Sun, Ao Cheng, Xingming Li, Xuanyu Ji, Hailun Lu, Runke Huang, Qingyong Hu

llms reinforcement-learning benchmarks

arXiv AI

Aug 5

Don't Peek at the Answer: Outcome-Masked Group Relative Policy Optimization for Label-Free RLVR

arXiv:2608. 03119v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves LLM reasoning but typically relies on ground-truth (GT) answers, limiting scalability.

By Yongshi Ye, Liang Zhang, Yidong Chen, Xiaodong Shi, Biao Fu

llms reinforcement-learning benchmarks

arXiv AI

Jun 2

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning

arXiv:2606. 01281v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as a powerful paradigm for enhancing the reasoning capabilities of large language models (LLMs).

By Yixiu Mao, Yun Qu, Qi Wang, Heming Zou, Xiangyang Ji

llms reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 22

RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts

arXiv:2607. 18470v1 Announce Type: cross Abstract: Group Relative Policy Optimization (GRPO) has shown strong effectiveness in reinforcement learning from verifiable feedback, where sampled rollouts can be compared within a group using task-provided correctness signals.

By Yuxin Xiong, Xunyi Jiang, Rohan Surana, Xintong Li, Sheldon Yu, Nikki Lijing Kuang, Ryan A. Rossi, Jingbo Shang, Tong Yu, Julian McAuley, Junda Wu

reinforcement-learning fine-tuning safety

Related stories

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning

Le Critique: Privileged Value Functions for LLM Reinforcement Learning

Advantage Collapse in Group Relative Policy Optimization: Diagnosis and Mitigation

Don't Peek at the Answer: Outcome-Masked Group Relative Policy Optimization for Label-Free RLVR

RLVR without Ineffective Samples: Group Prioritized Off-Policy Optimization for LLM Reasoning

RRPO: Reference-Relative Policy Optimization with Stratified Conditional Rollouts