This paper explores how specialist models, trained without explicit reasoning supervision, can still effectively transfer domain expertise to student models through implicit trajectory selection. Practitioners may care because this finding has implications for efficient and effective model distillation.
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The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking
Kyle Corbitt, founder of OpenPipe and leader of CoreWeave's serverless training team, provides a master class on reinforcement learning (RL) and custom fine-tuning for AI models. He explains how RL differs from supervised fine-tuning (SFT) …