Author

Takumi Shioda

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Preprint Jul 2026

Exact Action Values Are Not Enough: Rollout-Verified Reinforcement Fine-Tuning of a Reasoning Model for Multi-Zone VAV Control

This work first test whether a frontier reasoning model (an LLM trained to use additional inference-time computation) can achieve competitive VAV control from text without building-specific training, and whether TD3-guided reinforcement fine-tuning (RFT) can transfer control knowledge into a locally deployable open-weight model.

Takumi Shioda, Kohei Terashima, T. Nagai · 0 citations

Jul 2026

Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control

This study adapts an open-weight reasoning model through reinforcement learning with verifiable rewards (RLVR), which converts exact offline dynamic-programming action values into dense rewards for every candidate action to adapt open-weight reasoning models to building storage scheduling.

Takumi Shioda, Kohei Terashima, T. Nagai · arXiv.org · 1 citation