arXiv Machine Learning June 30, 2026 By Xian Wu, Haoran Li, Yuanqi Chu, Dongbin Zhao, Bin Wang

Reinforcement Learning with a Bilevel World-Model Architecture for Scan-Order Optimisation in Laser Directed Energy Deposition

Read the original on arXiv Machine Learning →

arXiv:2605. 25063v2 Announce Type: replace Abstract: Scan-order design in laser directed energy deposition (LDED) is a delayed, path-dependent thermo-mechanical decision problem, because sequence quality becomes observable only after the complete deposition and cooling cycle.

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

arXiv AI

Jun 29

When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?

arXiv:2605. 30719v2 Announce Type: replace-cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.

By Stephane Hatgis-Kessell, Emma Brunskill

llms reinforcement-learning robotics

arXiv Machine Learning

Jun 26

Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models

arXiv:2510. 09976v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models such as OpenVLA, Octo, and $\pi_0$ have shown strong generalization by leveraging large-scale demonstrations, yet their performance is still fundamentally constrained by the quality and coverage of supervised data.

By Mingyang Lyu, Yinqian Sun, Erliang Lin, Huangrui Li, Ruolin Chen, Feifei Zhao, Yi Zeng

diffusion reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 9

Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy

arXiv:2606. 08779v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a pivotal post-training paradigm, yet it frequently suffers from unpredictable sub-optimum performance or even training collapses.

By Jiashun Liu, Runze Liu, Xu Wan, Jing Liang, Hongyao Tang, Ling Pan

llms reinforcement-learning

arXiv Machine Learning

Jun 25

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning

arXiv:2605. 20256v2 Announce Type: replace Abstract: Reinforcement learning has become a cornerstone for aligning and unlocking the reasoning capabilities of large-scale models.

By Xikai Zhang, Yongzhi Li, Likang Xiao, Yingze Zhang, Yanhua Cheng, Quan Chen, Peng Jiang, Wenjun Wu, Liu Liu

reinforcement-learning

arXiv AI

Jun 18

STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability

arXiv:2606. 19236v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards algorithms like GRPO have emerged as the dominant post-training paradigm for complex reasoning in LLMs, yet commonly suffer from policy entropy collapse during training.

By Haipeng Luo, Qingfeng Sun, Songli Wu, Can Xu, Wenfeng Deng, Han Hu, Yansong Tang

llms agents reinforcement-learning

arXiv Machine Learning

Jun 5

Alpha-RTL: Test-Time Training for RTL Hardware Optimization

arXiv:2606. 05253v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in generating functionally correct register-transfer-level (RTL) hardware designs.

By Peilong Zhou, Zhirong Chen, Cangyuan Li, Haoyu Gao, Kaiyan Chang, Ziming Qu, Ying Wang

llms agents reinforcement-learning

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When are LLMs Sufficient Policy Optimizers for Sequential RL Tasks?

Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models

Reformulate LLM Reinforcement Learning for Efficient Training under Black-box Discrepancy

FBOS-RL: Feedback-Driven Bi-Objective Synergistic Reinforcement Learning

STARE: Surprisal-Guided Token-Level Advantage Reweighting for Policy Entropy Stability

Alpha-RTL: Test-Time Training for RTL Hardware Optimization