arXiv AI October 19, 2026 By Bangji Yang, Hongbo Ma, Ruihan Guo, Jiajun Fan, Xi Zhu, Ge Liu

Batched Contextual Reinforcement II: Does RLVR Need Long Rollout Windows?

arXiv:2610. 22887v1 Announce Type: cross Abstract: Long rollout windows make reinforcement learning with verifiable rewards (RLVR) expensive, yet simply shortening them can remove the correctness signal: when every single-problem rollout truncates before an answer, group-relative accuracy advantages vanish.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

reinforcement-learning

arXiv AI

Oct 15

Double-Rollout Reinforcement Learning with Sampling-based Reward Estimation

arXiv:2610.16593v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) implemented with Group Relative Policy Optimization (GRPO) has driven substantial progress in tra...

By Haifeng Qian, David Mosallanezhad, Christian Munley, Adithya Renduchintala, Oleksii Kuchaiev

llms reinforcement-learning

arXiv AI

Sep 30

Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training

arXiv:2609.37119v1 Announce Type: cross Abstract: Recent approaches to reinforcement learning (RL) post-training for large language models increasingly remove the critic to reduce training instabilit...

By Hongyang Li, Xiao Li, Caesar Wu, Said Mammar, Gr\'egoire Danoy, Pascal Bouvry

llms reinforcement-learning safety

arXiv Machine Learning

Sep 3

Cliff: Learning Process Rewards from the First Mistake

Cliff is a reward‑shaping method for reinforcement learning with verifiable rewards that identifies the first mistake in a language model’s reasoning process using an off‑the‑shelf teacher. By splitting each rollout into a correct prefix and an incorrect suffix, Cliff assigns token‑level advantages—positive for correct tokens and negative for the rest—providing fine‑grained supervision. Across 12 scenarios, Cliff outperforms on‑policy distillation by 15% and standard GRPO by 7%, even when the teacher is only modestly capable.

By Peixuan Han, Runhui Wang, Ketan Ramaneti, Jie Hao, Gerald Friedland, Chris Kong

llms reinforcement-learning efficiency

arXiv Machine Learning

Oct 15

Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling

arXiv:2610.20497v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) trains language models from checked task outcomes, but assigning a terminal reward to individual...

By Wei Chen, Yubing Wu, Zhou Xu, Haoyue Zhang, Delu Zeng, Jiahao Wang, Junmei Yang, Pan tiewen, Jingfeng Liu, Zhiheng Zhou, Bo Li, Min Chen

llms reinforcement-learning

arXiv Machine Learning

Jul 14

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

arXiv:2607. 11506v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) optimizes LLMs using sparse verifiable final-answer rewards.

By Xiaojian Liu, Han Xu, Jianqiang Xia, Zhixuan Li, Ke Xu, Yiwei Dai, Xinran Chen, Changwo Wu, Yuchen Li

llms reinforcement-learning

arXiv AI

Jun 11

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward

arXiv:2606. 11209v1 Announce Type: cross Abstract: Visual question answering increasingly requires multi-step reasoning.

By Jingpei Wu, Xiao Han, Weixiang Shen, Boer Zhang, Zifeng Ding, Volker Tresp

llms nlp reinforcement-learning fine-tuning multimodal benchmarks

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Related stories

Double-Rollout Reinforcement Learning with Sampling-based Reward Estimation

Unlocking the Critic: Reward-Free Policy Optimization for LLM Post-Training

Cliff: Learning Process Rewards from the First Mistake

Hindsight-Privileged RLVR: Single-Rollout Token Credit Without Group Sampling

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

ProcessThinker: Enhancing Multi-modal Large Language Models Reasoning via Rollout-based Process Reward