arXiv AI July 3, 2026 By Karthikeya Aditya Vissa, Sankalp Mane, Ananya Mantravadi, Harshit Rajgarhia, Abhishek Mukherji

Beyond Next-Token Prediction: An RLVR Proof of Concept for Tool-Use Agents on Atlassian Workflows

arXiv:2607. 01465v1 Announce Type: new Abstract: Large language models are trained to predict the next token, not to act inside a specific API.

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

arXiv AI

Jun 3

Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments

arXiv:2606. 03892v1 Announce Type: cross Abstract: Training LLMs to orchestrate multi-step tool calls is held back by three coupled obstacles: realistic stateful execution environments are costly to build, synthetic training queries are often detached from the server's actual state (so the generated tool calls fail to execute), and recall-based RL rewards incentivize verbose tool-calling patterns.

By Ibrahim Abdelaziz, Asim Munawar, Kinjal Basu, Maxwell Crouse, Chulaka Gunasekara, Suneet Katrekar, Pavan Kapanipathi

llms agents 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

Jul 21

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

arXiv:2607. 16204v1 Announce Type: new Abstract: Recent growth in reinforcement learning (RL) has surfaced a need for diverse, specialized training environments.

By Darshan Deshpande

llms agents diffusion reinforcement-learning fine-tuning efficiency safety

arXiv AI

6d ago

VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies

arXiv:2608. 12282v1 Announce Type: new Abstract: Agents deployed in enterprise settings must reason across structured APIs and document collections, yet existing benchmarks evaluate these capabilities in isolation.

By Ankita Rajaram Naik, Anupama Murthi, Benjamin Elder, Siyu Huo, Raavi Gupta, Abhinav Jain, Praveen Venkateswaran, Abdulhamid Adebayo, Danish Contractor

agents benchmarks

arXiv Machine Learning

Jul 7

Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models

arXiv:2607. 02869v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a promising paradigm for improving mathematical reasoning in language models.

By Anagha Radhakrishna Palandye, Rebecca Glick, Osheen Kaul

llms reinforcement-learning fine-tuning

arXiv AI

Jun 16

Understanding Diversity Collapse in RLVR via the Lens of Overtraining

arXiv:2606. 15455v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a key approach for enhancing the reasoning abilities of large language models.

By Suqin Yuan, Jinkun Chen, Jiyang Zheng, Muyang Li, Lei Feng, Dadong Wang, Tao Xiang, Tongliang Liu, Bo An

llms reinforcement-learning benchmarks

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Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments

SCOPE-RL: Optimizing Reasoning Paths Before and After Success

Masked Diffusion Language Models are Strong and Steerable Text-Based World Models for Agentic RL

VAKRA: Evaluating Multi-Hop Reasoning Across APIs and Retrieval Under Tool-Use Policies

Reward Granularity in RLVR: Comparing Process and Outcome Reward Structures for Mathematical Reasoning in Small Language Models

Understanding Diversity Collapse in RLVR via the Lens of Overtraining