The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking
2026-05-01 · 107 min · 21 entities
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Link in episode "The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking": https://getvcx.com/
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Link in episode "The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking": https://sequencehq.com/
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The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking
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Link in episode "The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking": https://getvcx.com/
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Link in episode "The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking": https://getvcx.com/
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Link in episode "The RL Fine-Tuning Playbook: CoreWeave's Kyle Corbitt on GRPO, Rubrics, Environments, Reward Hacking": https://sequencehq.com/
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Episode description as stored
Kyle Corbitt, founder of OpenPipe, breaks down reinforcement learning and custom fine-tuning for modern AI models. He explains how RL differs from supervised fine-tuning, why GRPO and LLM-as-judge post-training matter, and how these techniques can improve performance, latency, and cost on open source models. The conversation also covers reward hacking, evaluation design, LoRA adapters, and how Chinese labs are using distillation to fast-follow frontier models.
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