arXiv AI June 8, 2026 By Yibo Li, Zijie Lin, Ailin Deng, Xuan Zhang, Yufei He, Shuo Ji, Tri Cao, Bryan Hooi

Just-In-Time Reinforcement Learning: Continual Learning in LLM Agents Without Gradient Updates

arXiv:2601. 18510v2 Announce Type: replace-cross Abstract: While Large Language Model (LLM) agents excel at general tasks, they inherently struggle with continual adaptation due to the frozen weights after deployment.

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.

arXiv Machine Learning

Jul 7

RL Forgets! Towards Continual Policy Optimization

arXiv:2607. 04364v1 Announce Type: new Abstract: Continual post-training is becoming a central paradigm for adapting vision-language models to evolving tasks.

By Mao-Lin Luo, Zhe-Xu Wang, Zi-Hao Zhou, Bo Ye, Jian Zhao, Min-Ling Zhang, Tong Wei

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jul 10

When Does Continual Learning Require Learning

arXiv:2607. 07847v1 Announce Type: new Abstract: As large language models (LLMs) become increasingly capable, the next question is how can we enable models to continually learn?

By Anne Harrington, Nayan Saxena, Michael Murphy, Anastasia Borovykh, Zeyu Yun, Sridhar Kamath, Ara Eindra Kyi, Trevor Darrell, Jitendra Malik, Yutong Bai

llms agents reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Sep 15

LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents

arXiv:2609.14138v1 Announce Type: cross Abstract: As LLM agents become integrated into increasingly complex workflows, they must continually acquire new capabilities while retaining competence on pre...

By Siddharth Sharma, Nilesh Prasad Pandey, Onat Gungor, Tajana Rosing

llms agents benchmarks

arXiv AI

Jul 23

REGEN: Replay-recycling for Expert-to-Generalist distillation with Offline Reinforcement Learning

arXiv:2607. 19450v1 Announce Type: cross Abstract: Large-scale online reinforcement learning (RL) is the predominant means of eliciting advanced abilities including long-term reasoning and agentic tool use in large language models (LLMs).

By Yunjie Chen, Xiaoxin Chen, Fang Wang

llms agents reinforcement-learning efficiency

arXiv Machine Learning

Aug 11

Matching Accuracy, Different Geometry: Evolution Strategies vs GRPO in LLM Post-Training

arXiv:2604. 01499v2 Announce Type: replace Abstract: Evolution Strategies (ES) have emerged as a scalable gradient-free alternative to reinforcement learning based LLM fine-tuning, but it remains unclear whether comparable task performance implies comparable solutions in parameter space.

By William Hoy, Binxu Wang, Xu Pan

llms reinforcement-learning fine-tuning

arXiv AI

Jun 15

Rethinking the Trust Region in LLM Reinforcement Learning

arXiv:2602. 04879v3 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a cornerstone for fine-tuning Large Language Models (LLMs), with Proximal Policy Optimization (PPO) serving as the de facto standard algorithm.

By Penghui Qi, Xiangxin Zhou, Zichen Liu, Tianyu Pang, Chao Du, Min Lin, Wee Sun Lee

llms reinforcement-learning fine-tuning efficiency

One email a morning, machine-written

Related stories

RL Forgets! Towards Continual Policy Optimization

When Does Continual Learning Require Learning

LIMBO: Lifelong Inference-Time Memory and Budget Optimization for LLM Agents

REGEN: Replay-recycling for Expert-to-Generalist distillation with Offline Reinforcement Learning

Matching Accuracy, Different Geometry: Evolution Strategies vs GRPO in LLM Post-Training

Rethinking the Trust Region in LLM Reinforcement Learning