arXiv Machine Learning

Learning Agentic Time Series Forecasting via Trajectory Distillation and Memory-Based Reinforcement Learning

The paper introduces Cast‑R1, an 8‑billion‑parameter agentic forecasting model that learns to orchestrate analytical and prediction tools through trajectory distillation and multi‑turn reinforcement learning with structured memory. The training pipeline first generates multi‑turn interaction trajectories using a teacher LLM, then fine‑tunes the agent with supervised learning and group relative policy optimization, guided by rewards for accuracy and temporal structure. Experiments across several datasets show that Cast‑R1 effectively equips smaller language models with tool‑augmented, multi‑turn forecasting capabilities within structured workflows.

arXiv AI

2d ago

CastFlow: Bridging Evidence Acquisition and Accurate Prediction in Agentic Time Series Forecasting

CastFlow is a role‑specialized agentic forecasting framework that combines general‑purpose tool reasoning with specialized temporal forecasting. It uses an Experience Memory to guide a frozen LLM in acquiring evidence via a multi‑view toolkit, then a lightweight specialized LLM refines predictions through evidence‑grounded reasoning. The two‑stage training strategy—supervised fine‑tuning followed by reinforcement learning with verifiable rewards—optimizes the specialized model based on final forecasting outcomes, achieving superior performance on nine out of ten real‑world benchmarks.

By Bokai Pan, Mingyue Cheng, Xiaoyu Tao, Zhiding Liu, Shuo Yu, Yuchong Wu, Qi Liu, Defu Lian, Enhong Chen

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 25

Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMs

arXiv:2506.10630v4 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techni...

By Yitong Zhou, Yucong Luo, Mingyue Cheng, Qi Liu, Jiahao Wang, Daoyu Wang, Enhong Chen

llms reinforcement-learning fine-tuning safety

arXiv Machine Learning

Aug 3

Learning Stateful Predictive Knowledge From Experience

arXiv:2607. 28638v1 Announce Type: cross Abstract: As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights.

By Yan Song, Xidong Feng, Bo Liu, Xinyu Cui, Haotian Fu, Zichen Liu, Mengyue Yang, Cheng Deng, Jian Zhao, Jun Wang

llms agents nlp reinforcement-learning efficiency

arXiv AI

Jun 4

Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs

arXiv:2506. 10630v3 Announce Type: replace-cross Abstract: To advance time series forecasting (TSF), various methods have been proposed to improve prediction accuracy, evolving from statistical techniques to data-driven deep learning architectures.

By Yitong Zhou, Yucong Luo, Mingyue Cheng, Qi Liu, Jiahao Wang, Daoyu Wang, Enhong Chen

llms reinforcement-learning fine-tuning safety

arXiv AI

Aug 5

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.

By Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang, Huanjian Zhang, Tian Gao, Yaguo Liu, Qi Liu, Enhong Chen

llms agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 31

TAPO: Transition-Aware Policy Optimization for LLM Agents

arXiv:2607. 27973v1 Announce Type: new Abstract: Recently, Reinforcement Learning (RL) has emerged as a crucial paradigm for the post-training of Large Language Model (LLM) agents.

By Cong Li, Peixi Peng, Yisen Zhao, Xinyu Hu, Shudong Liu, Zhan Su, Zhuojian Li

llms agents reinforcement-learning

arXiv AI

Jun 29

Internalizing the Future: A Unified Agentic Training Paradigm for World Model Planning

arXiv:2606. 27483v1 Announce Type: new Abstract: Large language model (LLM) agents have demonstrated strong capability in sequential decision-making, yet they remains fundamentally reactive in long-horizon tasks.

By Xuan Zhang, Zhijian Zhou, Lingfeng Qiao, Yulei Qin, Ke Li, Xing Sun, Xiaoyu Tan, Chao Qu, Yuan Qi

llms agents reinforcement-learning fine-tuning

arXiv AI

Aug 19

PlanPO: Group Planning-Aware Policy Optimization for Multi-Turn Agentic LLMs

PlanPO introduces a group planning-aware policy optimization method for multi-turn agentic large language models, addressing the issue of advantage collapse caused by treating all successful trajectories equally. By incorporating coarse-to-fine advantage signals that reflect differences in trajectory and turn lengths, PlanPO encourages agents to learn generalizable planning and generation behaviors. Experiments show a 27.2% average improvement over GRPO on benchmarks such as ALFWorld, WebShop, and SciWorld, with minimal extra training cost.

By Dayang Liang, Liyuan He, Xuan Feng, Shuxin Li, Bo An, Yunlong Liu

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 13

LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation

arXiv:2608. 11967v1 Announce Type: cross Abstract: Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory.

By Zhixin Zhang, Xinke Jiang, Zhibang Yang, Weixuan Xu, Guohong Qiu, Xu Chu, Junfeng Zhao, Yasha Wang

llms rag agents reinforcement-learning efficiency benchmarks

arXiv AI

Sep 11

KairosAgent: Agentic Time Series Forecasting with Fused Semantic Reasoning

KairosAgent is an agentic framework that combines a large language model (LLM) reasoner with a time series foundation model (TSFM) forecaster to tackle cross‑domain multimodal time series forecasting. It dynamically invokes analytical tools to improve the LLM’s numerical comprehension and semantic reasoning, then fuses the reasoning outcomes into the TSFM pipeline for more accurate predictions. The approach is further enhanced by a curated large‑scale trajectory corpus and a reinforcement learning paradigm with multi‑turn refinement and turn‑level credit assignment, achieving superior zero‑shot forecasting performance.

By Kun Feng, Ziwei Shan, Yuchen Fang, Yiyang Tan, Sihan Lu, Shuqi Gu, Xingyu Lu, Lintao Ma, Kan Ren

llms agents reinforcement-learning multimodal

arXiv AI

Jul 28

The Physics of Multi-Turn Long-Horizon Planning: From Pre-training to Post-training via Single- and Multi-Teacher On-Policy Agentic Distillation

arXiv:2607. 24720v1 Announce Type: cross Abstract: Multi-turn long-horizon planning is critical for foundation model agents, yet how to fundamentally improve it remains unclear.

By Tianyi Men, Zhuoran Jin, Kang Liu, Jun Zhao

agents efficiency

arXiv AI

2d ago

AlphaBet: Reinforcement Learning for LLM Event Forecasting Agents

AlphaBet is a reinforcement learning framework that trains a single large language model to search for evidence, reason about it, and report probabilities for unresolved events across multiple pre-resolution dates. By scoring each dated forecast with a proper scoring rule, AlphaBet uses one resolved event to supervise decisions under varying information states, decomposing forecasting regret into evidence-acquisition and probability-reporting gaps. Experiments on GLM-4.5-Air show that AlphaBet improves Brier scores on Forecast-Dojo and FutureX, attributing gains to multi-date training and better information acquisition and probability reporting.

By Liqin Ye, Rongzhi Zhang, Xin Liu, Zheng Li, Yuan He, Qingyu Yin, Priyanka Nigam, Chao Zhang

llms agents reinforcement-learning

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