Master Thesis: Verifiable Reward RL (RLVR) for Multilingual Agentic Tool & Function Calling

As Sweden's national center for applied AI, we're on a mission to accelerate the use of AI to benefit our society, our competitiveness, and everyone living in Sweden. We drive impactful initiatives in areas such as healthcare, energy, and public services while pushing the boundaries of AI research in fields such as natural language processing, machine learning and AI security. Join us in harnessing the untapped value of AI to drive innovation and create sustainable value for Sweden. We are now looking for a master thesis student to join our team. Introduction For LLMs to serve effectively in public administration and enterprise workflows, they must accurately invoke external APIs and structured tools (Function Calling)[cite: 10, 14]. While SFT provides basic tool-use syntax, Reinforcement Learning with Verifiable Rewards (RLVR) is required to optimize multi-step agentic trajectories and error recovery. Project Background and Problem Statement OpenEuroLLM is developing the oellm-rlvr framework to support agentic and function-calling rollouts.

• This thesis asks: Does RLVR training on tool-execution environments (e.g., Tau-Bench, BFCL) significantly improve multi-step function-calling accuracy and schema adherence in Swedish and English compared to SFT alone?

Outline

• Literature Study: Review agentic environment design, function-calling benchmarks (BFCL, Terminal-Bench), and RLVR mechanics.
• Implementation: Adapt oellm-rlvr to execute tool-use rollouts with deterministic execution verifiers on Prelude 9B.
• Evaluation: Benchmark tool-calling precision, schema validity, and Model FLOPs Utilization (MFU) across single-turn and multi-turn administrative workflow.

Who we’re looking for We are seeking curious, self-driven MSc students eager to work at the frontier of open-weight European AI research (LLMs). You thrive on empirical discovery, design rigorous experiments, and let data challenge your assumptions.

• Ongoing Master’s studies in Computer Science, Data Science, Machine Learning, Engineering Physics, or a related quantitative field.
• Proficiency in Python and hands-on experience with modern deep learning frameworks (PyTorch, Hugging Face ecosystem).
• Familiarity with LLM post-training alignment (e.g., SFT, DPO, RLHF/RLVR) or context-extension, alongside comfort running distributed GPU training in Linux/HPC environments.

At AI Sweden, we are committed to building diverse and inclusive teams. Some positions may be subject to export control regulations, which means that specific requirements may apply. Why should you do your thesis with AI Sweden? Doing your thesis at AI Sweden means working alongside leading AI scientists and change leaders. AI Sweden is Sweden’s National Center for AI, we drive research questions that have both a long shelf-life and are widely applicable to Swedish industry and the public sector. We aim for publications at the most competitive venues and celebrate a culture of research excellence. As an organization, we’re uniquely positioned at the sweet spot of governmental influence and startup agility. Small enough to stay adaptive and have fun but backed by and in close contact with both the government, academia and private and public sector. Practical details Location: Hybrid (Gothenburg / Stockholm) or Remote. Application Deadline: 2026-11-20 (rolling selection – position may be filled earlier). Start Date: January 2027 Contact If you have any questions or thoughts, don’t hesitate to contact: Birger Moëll, Senior Research Scientist Niclas Hertzberg, AI Engineer AI Sweden does not accept unsolicited support and kindly ask not to be contacted by any advertisement agents, recruitment agencies or manning companies. References [1] OpenEuroLLM Consortium, "oellm-rlvr Agentic & Function Calling Environment," 2026. [2] Gorilla Team, "Berkeley Function Calling Leaderboard (BFCL)," 2024.