Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control
Buildings are expected to shift cooling loads in response to grid conditions. Thermal energy storage (TES) enables this shift, but scheduling it well requires planning hours ahead under storage constraints. Model predictive control (MPC) and reinforcement learning are difficult to scale across buildings. This study instead adapts an open-weight reasoning model through reinforcement learning with verifiable rewards (RLVR). We convert exact offline dynamic-programming (DP) action values into dense rewards for every candidate action. Using only 30 training prompts, reinforcement fine-tuning (RFT)
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- PossiblePossibly related (embedding) · 47%AgileRL/AgileRL →
- PossiblePossibly related (embedding) · 46%hscspring/rl-llm-nlp →
- PossiblePossibly related (embedding) · 46%airbus/scikit-decide →
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet →
“Fuzzy title match (0.94): “Verifier-Based Reinforcement Fine-Tuning of Reasoning Models” ≈ “lllyasviel/ControlNet””
- FuzzySimilar title/name (fuzzy) · 87%lllyasviel/ControlNet-v1-1 →
“Fuzzy title match (0.94): “Verifier-Based Reinforcement Fine-Tuning of Reasoning Models” ≈ “lllyasviel/ControlNet-v1-1””
- LinkedLinked via arxiv author · 85%Takumi Shioda →
“Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control”
- LinkedLinked via arxiv author · 85%Kohei Terashima →
“Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control”
- LinkedLinked via arxiv author · 85%Tatsuo Nagai →
“Verifier-Based Reinforcement Fine-Tuning of Reasoning Models for Thermal Energy Storage Control”