Reinforcement learning teachers yield stronger student models in merging studies

A new study published on arXiv, titled "Train4Merge: A Controlled Single-Teacher Study of RL vs. SFT Teachers for OPD-Based Model Merging," investigates the effectiveness of different training algorithms for creating expert models used in on-policy distillation (OPD) for model merging. Researchers compared supervised fine-tuning (SFT) and reinforcement learning (RL) teachers, finding that RL teachers consistently produced stronger student models across agentic, reasoning, and perception domains. The study observed that RL-guided students recovered more of their teacher's performance gains, with one instance even surpassing the teacher's performance. AI

IMPACT This research suggests reinforcement learning may be a more effective method for training expert models in model merging scenarios, potentially leading to improved student model performance.

RANK_REASON The cluster contains a research paper detailing a new study on model merging techniques. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Jingyuan Huang
• Qwen3.5-9B
• reinforcement learning
• supervised fine-tuning
• Train4Merge

• paper
• model release

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