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  "text": "Reasoning From Scratch round 8, the finale!\nThis last lecture in the series covers distillation as an efficient alternative to the previous reinforcement learning training runs.\n\n(Planning to take a brief break, but I will be back!)\n\n00:00 Introduction and series recap\n01:07 Why distill a reasoning model?\n02:51 Soft vs. hard distillation\n11:06 On-policy vs. off-policy distillation\n18:09 Preparing data for reasoning distillation\n22:35 Loading the DeepSeek R1 MATH dataset\n28:04 Inspecting reasoning traces and final answers\n31:27 Tokenization and chat templates\n35:58 Pre-tokenizing the training dataset\n39:01 Sequence lengths and the 2,048-token limit\n42:38 Creating training and validation splits\n45:32 Loading the Qwen3-0.6B base model\n46:24 Computing answer-token log probabilities\n54:10 Cross-entropy loss for distillation\n57:25 Training and validation loss helpers\n1:02:03 Implementing the distillation training loop\n1:07:26 Running the standalone training script\n1:09:41 Plotting training and validation losses\n1:11:31 Evaluating checkpoints on MATH-500\n1:14:09 Comparing teachers and distillation results\n1:16:34 Future directions for reasoning models\n1:19:41 Bonus exercises and next steps",
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