Reinforcement learning

Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.

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arXiv AI

Aug 5

Target-Aligned Fusion for Decision-Sequence Learning under Dynamics Shift

arXiv:2511. 09173v3 Announce Type: replace-cross Abstract: External trajectories can improve offline decision-sequence learning, but dynamics shift may make some source subsequences inconsistent with the target environment.

By Guojian Wang, Quinson Hon, Xuyang Chen, Lin Zhao

llms reinforcement-learning safety

arXiv Machine Learning

Aug 5

Don't Walk the Line: Boundary Guidance for Filtered Generation

arXiv:2510. 11834v3 Announce Type: replace Abstract: Generative models are increasingly paired with safety classifiers that filter harmful or undesirable outputs.

By Sarah Ball, Andreas Haupt

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 5

LLM-Derived Priors for Thompson Sampling in Cold-Start Comment Recommendation

arXiv:2608. 03382v1 Announce Type: cross Abstract: Multi-armed bandit algorithms, especially Thompson sampling, are widely used in online recommendation.

By Eugene Lee, Oseong Choi, Byungsoo Kang, Taeyeong Jang

llms reinforcement-learning safety

arXiv Machine Learning

Aug 5

Beyond Solving: Prescriptive Probing for Neural Routing Solvers

arXiv:2602. 07216v2 Announce Type: replace Abstract: Neural combinatorial optimization (NCO) trains fast heuristics for routing problems, but planners often need more than a single solve: they ask which stop to drop, which transition to preserve, or which subset of stops to remove if a route is infeasible.

By Reuben Narad, L\'eonard Boussioux, Michael Wagner

reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 5

Any-OPD: Heterogeneous On-Policy Distillation for Flow-Matching Models via Representation-Space Bridging

arXiv:2608. 03316v1 Announce Type: new Abstract: On-policy distillation, in which a teacher corrects samples that the student itself generates, presupposes that the two models speak the same language: identical VAE latents, matching architectures, and a common timestep grid.

By Siming Fu, Zheming Fu, Ruizhe He, Hualiang Wang, Jie Huang, Xiaoxiao Ma, Mingchen Zhong, Weihu Huang, Xiaoxuan He, Haojun Xu

reinforcement-learning efficiency

arXiv Machine Learning

Aug 5

DiagLoop: A Counterfactual Data Flywheel with Stage-Localized Reinforcement for Diagnostic LLMs

arXiv:2608. 03674v1 Announce Type: new Abstract: Causal diagnostic models must explain how conclusions follow from evidence because diagnoses guide repairs and treatments.

By Jian Zhang, Bingyi Wang, Yizhi Liu

llms reinforcement-learning

arXiv AI

Aug 5

Hybrid LLM-Augmented Reinforcement Learning Agents for Complex Sequential Decision Tasks

arXiv:2608. 03502v1 Announce Type: new Abstract: Large Language Models (LLMs) have recently shown strong capabilities in reasoning, planning, and tool-use, enabling new forms of autonomous agents.

By Christophe D. Hounwanou, John Emeka Eze, Ya\'e Ulrich Gaba

llms agents reinforcement-learning

arXiv AI

Aug 5

BODHI: Do LLMs Branch Out and Discover Heterogeneous Inferences?

arXiv:2608. 02867v1 Announce Type: cross Abstract: Although reinforcement learning with verifiable rewards (RLVR) has improved the performance of large language models (LLMs) across a variety of reasoning tasks, there is significant debate as to whether RLVR expands the reasoning capability boundary, or just improves sampling efficiency.

By Soumadeep Saha, Krish Sharma, Akshay Chaturvedi, Nicholas Asher

llms reinforcement-learning

arXiv AI

Aug 5

Enhancing VLM Reward Models Through Structure-Aware Fine-Tuning

arXiv:2608. 03875v1 Announce Type: cross Abstract: Designing effective reward functions remains a major bottleneck in Reinforcement Learning (RL).

By Pyrros Koussios, Chenhao Li, Xin Chen, Andreas Krause

llms diffusion reinforcement-learning fine-tuning multimodal safety

arXiv Machine Learning

Aug 5

SFT Conflicts, RL Coexists: A Theoretical and Empirical Analysis of Multi-Task Learning for LLMs

arXiv:2608. 03573v1 Announce Type: cross Abstract: Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) exhibit fundamentally different behaviors in enhancing multi-task reasoning for large language models (LLMs).

By Kejian Zhu, Zhuoran Jin, Shangqing Tu, Hongbang Yuan, Yushi Bai, Kang Liu, Juanzi Li, Jun Zhao

llms reinforcement-learning fine-tuning

arXiv AI

Aug 5

Improved Quantum Algorithms for Reinforcement Learning Under a Generative Model

arXiv:2608. 02826v1 Announce Type: cross Abstract: Reinforcement learning is a subfield of machine learning that studies how an agent interacts with an environment in order to extract as large a reward as possible.

By Joao F. Doriguello

agents reinforcement-learning

arXiv AI

Aug 5

SpatialCLI: Learning to Reason With Spatial Tools, Then Without Them

arXiv:2607. 27703v2 Announce Type: replace Abstract: Vision-language models (VLMs) are increasingly used in embodied agents to interpret visual inputs, reason about spatial relationships, and make task-level decisions based on that reasoning.

By Yang Zhou, Zixuan Huang, Sunzhu Li, Zhuo Yang, Chen Zhang, Shunian Chen, Caijun Yan, Jianyao Xu, Shunyu Liu, Weijie Fu, Peiliang Li, Xiaozhi Chen, Yuxiang Cai

llms agents computer-vision reinforcement-learning robotics fine-tuning multimodal benchmarks

arXiv Machine Learning

Aug 5

When RL Meets Adaptive Speculative Training: A Unified Training-Serving System

arXiv:2602. 06932v5 Announce Type: replace Abstract: Speculative decoding can significantly accelerate LLM serving, yet most deployments today disentangle speculator training from serving, treating speculator training as a standalone offline modeling problem.

By Junxiong Wang, Fengxiang Bie, Jisen Li, Zhongzhu Zhou, Zelei Shao, Yubo Wang, Yinghui Liu, Qingyang Wu, Avner May, Sri Yanamandra, Ce Zhang, Tri Dao, Percy Liang, Ben Athiwaratkun, Shuaiwen Leon Song, Chenfeng Xu, Xiaoxia Wu

llms reinforcement-learning

arXiv AI

Aug 5

PLAN: Parallel Liquid-Inspired Approximation Network for Efficient Representation Learning in Flexible Job Shop Scheduling

arXiv:2608. 03041v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) approaches for flexible job shop scheduling (FJSP) heavily rely on attention-centric architectures to achieve state-of-the-art performance.

By Dhivya Dharshini Kannan, Wei Zhang, Jieyi Bi, Yingpeng Du, Tianjun Wei, Jie Zhang, Zuming Liu, Anupam Trivedi

llms reinforcement-learning efficiency benchmarks

arXiv AI

Aug 5

LLM Serving in the Wild: An Empirical Study of Frameworks, Methods, and System Designs

arXiv:2608. 03036v1 Announce Type: cross Abstract: Large Language Models (LLMs) are integrated into software systems and AI services, making efficient LLM serving a concern for software engineering.

By Forough Majidi, Mohammad Mehdi Morovati, Foutse Khomh, Heng Li

llms reinforcement-learning efficiency multimodal

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

Aug 5

Token Buncher: Shielding LLMs from Harmful Reinforcement Learning Fine-Tuning

arXiv:2508. 20697v4 Announce Type: replace Abstract: As large language models (LLMs) continue to grow in capability, so do the risks of harmful misuse through fine-tuning.

By Weitao Feng, Lixu Wang, Peizhuo Lv, Tianyi Wei, Jie Zhang, Chongyang Gao, Sinong Zhan, Wei Dong

llms reinforcement-learning fine-tuning safety

arXiv AI

Aug 5

Training Documents Reranker with Search Rubrics for Deep Research Agent

arXiv:2608. 03527v1 Announce Type: cross Abstract: Retrieval systems help deep research agents generate high-quality answers by providing relevant documents.

By Wenhan Liu, Yu Lu, Qiaolin Xia, Hui Xu, Tong Zhao, Jian Xi, Yutao Zhu, Haijin Liang, Haibo Shi, Hao Wang, Zhicheng Dou

llms rag agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Aug 5

When Search Teaches Style: Causal Internalization of Tactical Priors in AlphaZero

arXiv:2504. 14636v3 Announce Type: replace-cross Abstract: AlphaZero is normally evaluated as one agent: a policy-value network fused with Monte Carlo tree search.

By Ruitong Li, Binjie Guo, Aisheng Mo, Guowei Su, Han Wang, Jie Li, Ru Zhang

agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Aug 5

Improving Sample Efficiency in Multi-Agent Reinforcement Learning for Simulated Football Games via Exploration

arXiv:2503. 13077v2 Announce Type: replace Abstract: Multi-agent reinforcement learning has shown promise in learning cooperative behaviors in team-based environments.

By Amir Baghi, Jens Sj\"olund, Joakim Bergdahl, Linus Gissl\'en, Alessandro Sestini

agents reinforcement-learning efficiency benchmarks

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